Intelligent Approaches in Locomotion - A Review
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paper_title: Evolving Swimming Controllers for a Simulated Lamprey with Inspiration from Neurobiology

paper_content:

This paper presents how neural swimming controllers for a simulated lamprey can be developed using evolutionary algorithms. A genetic algorithm is used for evolving the architecture of a connectionist model which determines the muscular activity of a simulated body in interaction with water. This work is inspired by the biological model developed by Ekeberg which repro duces the central pattern generator observed in the real lamprey (Ekeberg, 1993). In evolving artificial controllers, we demonstrate that a genetic algorithm can be an interesting design tech nique for neural controllers and that there exist alternative solutions to the biological connectiv ity. A variety of neural controllers are evolved which can produce the pattern of oscillations necessary for swimming. These patterns can be modulated through the external excitation ap plied to the network in order to vary the speed and the direction of swimming. The best evolved controllers cover larger ranges of frequencies, phase lags and speeds of s...
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paper_title: An effective trajectory generation method for bipedal walking

paper_content:

This paper presents the virtual height inverted pendulum mode (VHIPM), which is a simple and effective trajectory generation method for the stable walking of biped robots. VHIPM, which is based on the inverted pendulum mode (IPM), can significantly reduce the zero moment point (ZMP) error by adjusting the height in the inverted pendulum. We show the relationship between VHIPM and other popular trajectory generation methods, and compare the ZMP errors in walking when trajectories are generated by various methods including VHIPM. We also investigate the sensitivity of the ZMP error in VHIPM to the step length, walking period and mass distribution of a robot. The simulation results show that VHIPM significantly reduces the ZMP errors compared to other methods under various circumstances.
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paper_title: Research on gait planning of artificial leg based on central pattern generator

paper_content:

Biped robot with heterogeneous legs (BRHL) is a novel robot model, which consists of an artificial leg and an intelligent bionic leg. The artificial leg is used to simulate the amputeepsilas healthy leg and the bionic leg works as the intelligent artificial limb. To describe the present gait of the healthy leg and make intelligent bionic leg follow the walking of artificial leg in all phases is the target of BRHLpsilas research. So gait planning of artificial leg is the emphasis of BRHLpsilas research. This paper uses the model of central pattern generator (CPG) in the research of artificial legpsilas gait planning from the point of biology. To obtain natural and robust walking pattern, genetic algorithm is used to optimize parameters of the CPG network model and the fitness function is formulated based on zero moment point (ZMP). Simulation results testify the feasibility of this method.
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paper_title: Evolution of Central Pattern Generators for Bipedal Walking in a Real-Time Physics Environment

paper_content:

We describe an evolutionary approach to the control problem of bipedal walking. Using a full rigid-body simulation of a biped, it was possible to evolve recurrent neural networks that controlled stable straight-line walking on a planar surface. No proprioceptive information was necessary in order to achieve this task. Furthermore, simple sensory input to locate a sound source was integrated to achieve directional walking. To our knowledge, this is the first work that demonstrates the application of evolutionary optimization to 3D physically simulated biped locomotion.
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paper_title: Humanoid robotics platforms developed in HRP

paper_content:

Abstract This paper presents humanoid robotics platform that consists of a humanoid robot and an open architecture software platform developed in METI’s Humanoid Robotics Project (HRP). The final version of the robot, called HRP-2, has 1540 mm height, 58 kg weight and 30 degrees of the freedom. The software platform includes a dynamics simulator and motion controllers of the robot for biped locomotion, falling and getting up motions. The platform has been used to develop various applications and is expected to initiate more humanoid robotics research.
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paper_title: A Biologically Inspired Biped Locomotion Strategy for Humanoid Robots: Modulation of Sinusoidal Patterns by a Coupled Oscillator Model

paper_content:

Biological systems seem to have a simpler but more robust locomotion strategy than that of the existing biped walking controllers for humanoid robots. We show that a humanoid robot can step and walk using simple sinusoidal desired joint trajectories with their phase adjusted by a coupled oscillator model. We use the center-of-pressure location and velocity to detect the phase of the lateral robot dynamics. This phase information is used to modulate the desired joint trajectories. We do not explicitly use dynamical parameters of the humanoid robot. We hypothesize that a similar mechanism may exist in biological systems. We applied the proposed biologically inspired control strategy to our newly developed human-sized humanoid robot computational brain (CB) and a small size humanoid robot, enabling them to generate successful stepping and walking patterns.
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paper_title: Development of Adaptive Modular Active Leg (AMAL) using bipedal robotics technology

paper_content:

The objective of the work presented here is to develop a low cost active above knee prosthetic device exploiting bipedal robotics technology which will work utilizing the available biological motor control circuit properly integrated with a Central Pattern Generator (CPG) based control scheme. The approach is completely different from the existing Active Prosthetic devices, designed primarily as standalone systems utilizing multiple sensors and embedded rigid control schemes. In this research, first we designed a fuzzy logic based methodology for offering suitable gait pattern for an amputee, followed by formulating a suitable algorithm for designing a CPG, based on Rayleigh's oscillator. An indigenous probe, Humanoid Gait Oscillator Detector (HGOD) has been designed for capturing gait patterns from various individuals of different height, weight and age. These data are used to design a Fuzzy inference system which generates most suitable gait pattern for an amputee. The output of the Fuzzy inference system is used for designing a CPG best suitable for the amputee. We then developed a CPG based control scheme for calculating the damping profile in real time for maneuvering a prosthetic device called AMAL (Adaptive Modular Active Leg). Also a number of simulation results are presented which show the stable behavior of knee and hip angles and determine the stable limit cycles of the network.
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paper_title: Dynamic response for motion capture animation

paper_content:

Human motion capture embeds rich detail and style which is difficult to generate with competing animation synthesis technologies. However, such recorded data requires principled means for creating responses in unpredicted situations, for example reactions immediately following impact. This paper introduces a novel technique for incorporating unexpected impacts into a motion capture-driven animation system through the combination of a physical simulation which responds to contact forces and a specialized search routine which determines the best plausible re-entry into motion library playback following the impact. Using an actuated dynamic model, our system generates a physics-based response while connecting motion capture segments. Our method allows characters to respond to unexpected changes in the environment based on the specific dynamic effects of a given contact while also taking advantage of the realistic movement made available through motion capture. We show the results of our system under various conditions and with varying responses using martial arts motion capture as a testbed.
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paper_title: Evolving Swimming Controllers for a Simulated Lamprey with Inspiration from Neurobiology

paper_content:

This paper presents how neural swimming controllers for a simulated lamprey can be developed using evolutionary algorithms. A genetic algorithm is used for evolving the architecture of a connectionist model which determines the muscular activity of a simulated body in interaction with water. This work is inspired by the biological model developed by Ekeberg which repro duces the central pattern generator observed in the real lamprey (Ekeberg, 1993). In evolving artificial controllers, we demonstrate that a genetic algorithm can be an interesting design tech nique for neural controllers and that there exist alternative solutions to the biological connectiv ity. A variety of neural controllers are evolved which can produce the pattern of oscillations necessary for swimming. These patterns can be modulated through the external excitation ap plied to the network in order to vary the speed and the direction of swimming. The best evolved controllers cover larger ranges of frequencies, phase lags and speeds of s...
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paper_title: On the mechanics of natural compliance in frictional contacts and its effect on grasp stiffness and stability

paper_content:

This paper considers the effect of natural material compliance on the stiffness and stability of frictional multi-contact grasps and fixtures. The contact preload profile is a key parameter in the nonlinear compliance laws governing such contacts. The paper introduces the Hertz-Walton contact compliance model which is valid for linear contact loading profiles. The model is specified in a lumped parameter form suitable for on-line grasping applications, and is entirely determined by the contact friction and by the material and geometric properties of the contacting bodies. The model predicts an asymmetric stiffening of the tangential reaction force as the normal load at the contact increases. As a result, the composite stiffness matrix of multi-contact grasps governed by natural compliance effects is asymmetric, indicating that these contact arrangements are not governed by any potential energy function. Based on the compliant grasp dynamics, the paper derives rules indicating which contact point locations and what preload profiles guarantee grasp and fixture stability. The paper also describes preliminary experiments supporting the contact model predictions.
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paper_title: A pattern generator of humanoid robots walking on a rough terrain using a handrail

paper_content:

This paper presents a motion pattern generator of humanoid robots that walks on a flat plane, steps and a rough terrain. It is guaranteed rigorously that the desired contact between a humanoid robot and terrain should be maintained by keeping the contact wrench sum between them inside the contact wrench cone under the sufficient friction assumption. A walking pattern is generated by solving the contact wrench equations and by applying the resolved momentum control.
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paper_title: Incremental Learning and Memory Consolidation of Whole Body Human Motion Primitives

paper_content:

The ability to learn during continuous and on-line observation would be advantageous for humanoid robots, as it would enable them to learn during co-location and interaction in the human environment. However, when motions are being learned and clustered on-line, there is a trade-off between classification accuracy and the number of training examples, resulting in potential misclassifications both at the motion and hierarchy formation level. This article presents an approach enabling fast on-line incremental learning, combined with an incremental memory consolidation process correcting initial misclassifications and errors in organization, to improve the stability and accuracy of the learned motions, analogous to the memory consolidation process following motor learning observed in humans. Following initial organization, motions are randomly selected for reclassification, at both low and high levels of the hierarchy. If a better reclassification is found, the knowledge structure is reorganized to comply. The approach is validated during incremental acquisition of a motion database containing a variety of full body motions.1
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paper_title: Design of a novel central pattern generator and the hebbian motion learning

paper_content:

In this paper, we propose a new CPG model and hebbian learning rule for the CPG. The output of the proposed CPG is determined by only phase differences of synchronization of component oscillators. A phase synchronization can be regarded as an adaptive behavior to an environment, so the CPG has an adaptavility despite of only simple connections to the environment. We also propose a motion learning rule for the proposed CPG. Since the rule is described by only simple signal processing, so that it can be realized by electric circuits easily, it will be able to be used efficiently for high degree of freedom robots.
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paper_title: Dynamically balanced optimal gaits of a ditch-crossing biped robot

paper_content:

This paper deals with the generation of dynamically balanced gaits of a ditch-crossing biped robot having seven degrees of freedom (DOFs). Three different approaches, namely analytical, neural network (NN)-based and fuzzy logic (FL)-based, have been developed to solve the said problem. The former deals with the analytical modeling of the ditch-crossing gait of a biped robot, whereas the latter two approaches aim to maximize the dynamic balance margin of the robot and minimize the power consumption during locomotion, after satisfying a constraint stating that the changes of joint torques should lie within a pre-specified value to ensure its smooth walking. It is to be noted that the power consumption and dynamic balance of the robot are also dependent on the position of the masses on various links and the trajectory followed by the hip joint. A genetic algorithm (GA) is used to provide training off-line, to the NN-based and FL-based gait planners developed. Once optimized, the planners will be able to generate the optimal gaits on-line. Both the NN-based and FL-based gait planners are able to generate more balanced gaits and that, too, at the cost of lower power consumption compared to those yielded by the analytical approach. The NN-based and FL-based approaches are found to be more adaptive compared to the other approach in generating the gaits of the biped robot.
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paper_title: An effective trajectory generation method for bipedal walking

paper_content:

This paper presents the virtual height inverted pendulum mode (VHIPM), which is a simple and effective trajectory generation method for the stable walking of biped robots. VHIPM, which is based on the inverted pendulum mode (IPM), can significantly reduce the zero moment point (ZMP) error by adjusting the height in the inverted pendulum. We show the relationship between VHIPM and other popular trajectory generation methods, and compare the ZMP errors in walking when trajectories are generated by various methods including VHIPM. We also investigate the sensitivity of the ZMP error in VHIPM to the step length, walking period and mass distribution of a robot. The simulation results show that VHIPM significantly reduces the ZMP errors compared to other methods under various circumstances.
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paper_title: Probabilistic Balance Monitoring for Bipedal Robots

paper_content:

In this paper, a probability-based balance monitoring concept for humanoid robots is proposed. Two algorithms are presented that allow us to distinguish between exceptional situations and normal operations. The first classification approach uses Gaussian-Mixture-Models (GMM) to describe the distribution of the robot's sensor data for typical situations such as stable walking or falling down. With the GMM it is possible to state the probability of the robot being in one of the known situations. The concept of the second algorithm is based on Hidden-Markov-Models (HMM). The objective is to detect and classify unstable situations by means of their typical sequences in the robot's sensor data. When appropriate reflex motions are linked to the critical situations, the robot can prevent most falls or is at least able to execute a controlled falling motion. The proposed algorithms are verified by simulations and experiments with our bipedal robot BARt-UH.
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paper_title: Embodied Symbol Emergence Based on Mimesis Theory

paper_content:

“Mimesis” theory focused in the cognitive science field and “mirror neurons” found in the biology field show that the behavior generation process is not independent of the behavior cognition process. The generation and cognition processes have a close relationship with each other. During the behavioral imitation period, a human being does not practice simple joint coordinate transformation, but will acknowledge the parents’ behavior. It understands the behavior after abstraction as symbols, and will generate its self-behavior. Focusing on these facts, we propose a new method which carries out the behavior cognition and behavior generation processes at the same time. We also propose a mathematical model based on hidden Markov models in order to integrate four abilities: (1) symbol emergence; (2) behavior recognition; (3) self-behavior generation; (4) acquiring the motion primitives. Finally, the feasibility of this method is shown through several experiments on a humanoid robot.
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paper_title: Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains

paper_content:

This paper describes a novel approach for autonomous and incremental learning of motion pattern primitives by observation of human motion. Human motion patterns are abstracted into a dynamic stochastic model, which can be used for both subsequent motion recognition and generation, analogous to the mirror neuron hypothesis in primates. The model size is adaptable based on the discrimination requirements in the associated region of the current knowledge base. A new algorithm for sequentially training the Markov chains is developed, to reduce the computation cost during model adaptation. As new motion patterns are observed, they are incrementally grouped together using hierarchical agglomerative clustering based on their relative distance in the model space. The clustering algorithm forms a tree structure, with specialized motions at the tree leaves, and generalized motions closer to the root. The generated tree structure will depend on the type of training data provided, so that the most specialized motions will be those for which the most training has been received. Tests with motion capture data for a variety of motion primitives demonstrate the efficacy of the algorithm.
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paper_title: Application of Genetic Algorithms for biped robot gait synthesis optimization during walking and going up-stairs

paper_content:

Selecting an appropriate gait can reduce consumed energy by a biped robot. In this paper, a Genetic Algorithm gait synthesis method is proposed, which generates the angle trajectories based on the minimum consumed energy and minimum torque change. The gait synthesis is considered for two cases: walking and going up-stairs. The proposed method can be applied for a wide range of step lengths and step times during walking; or step lengths, stair heights and step times for going up-stairs. The angle trajectories are generated without neglecting the stability of the biped robot. The angle trajectories can be generated for other tasks to be performed by the biped robot, like going down-stairs, overcoming obstacles, etc. In order to verify the effectiveness of the proposed method, the results for minimum consumed energy and minimum torque change are compared. A Radial Basis Function Neural Network is considered for the real-time application. Simulations are realized based upon the parameters of the 'Bonten-Maru ...
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paper_title: Biped robot design powered by antagonistic pneumatic actuators for multi-modal locomotion

paper_content:

An antagonistic muscle mechanism that regulates joint compliance contributes enormously to human dynamic locomotion. Antagonism is considered to be the key for realizing more than one locomotion mode. In this paper, we demonstrate how antagonistic pneumatic actuators can be utilized to achieve three dynamic locomotion modes (walking, jumping, and running) in a biped robot. Firstly, we discuss the contribution of joint compliance to dynamic locomotion, which highlights the importance of tunable compliance. Secondly, we introduce the design of a biped robot powered by antagonistic pneumatic actuators. Lastly, we apply simple feedforward controllers for realizing walking, jumping, and running and confirm the contribution of joint compliance to such multimodal dynamic locomotion. Based on the results, we can conclude that the antagonistic pneumatic actuators are superior candidates for constructing a human-like dynamic locomotor.
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paper_title: A combined potential function and graph search approach for free gait generation of quadruped robots

paper_content:

This paper presents an algorithm for planning the foothold positions of quadruped robots on irregular terrain. The input to the algorithm is the robot kinematics, the terrain geometry, a required motion path, as well as initial posture. Our goal is to develop general algorithm that navigate quadruped robots quasi-statically over rough terrain, using an APF (Artificial Potential Field) and graph searching. The algorithm is planning a sequence set of footholds that navigates the robot along the required path with controllable motion characteristics. Simulations results demonstrate the algorithm in a planner environment.
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paper_title: Integration of linguistic and numerical information for biped control

paper_content:

Bipedal locomotion is an important hallmark of human evolution. Despite of complex control systems, human locomotion is characterized by smooth, regular, and repeating movements. Therefore, there is the potential for applying human locomotion strategies and any knowledge available to the biped control. In order to make the most use of the information available, a linguistic-numerical integration-based biped control method is proposed in this paper. The numerical data from biped measuring instruments, and the linguistic rules obtained from intuitive walking knowledge and biomechanics study have been classified into four categories: direct rules, direct data, indirect rules, and indirect data. Based on inverse learning and data fusion theory, two simple and intuitive integration schemes are proposed to integrate linguistic and numerical information with various forms, such as direct and indirect. One is neurofuzzy-based integration, and another is fuzzy rules extraction-based integration. The simulation results show that the biped gait and joint control performance can be significantly improved by the prescribed synergy method-based neurofuzzy gait synthesis and fuzzy rules extraction-based joint control strategies using linguistic and numerical integrated information.
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paper_title: Experimental Validation of a Framework for the Design of Controllers that Induce Stable Walking in Planar Bipeds

paper_content:

In this paper we present the experimental validation of a framework for the systematic design, analysis, and performance enhancement of controllers that induce stable walking in N -link underactuated planar biped robots. Controllers designed via this framework act by enforcing virtual constraints—holonomic constraints imposed via feedback—on the robot’s configuration, which create an attracting two-dimensional invariant set in the full walking model’s state space. Stability properties of resulting walking motions are easily analyzed in terms of a two-dimensional subdynamic of the full walking model. A practical introduction to and interpretation of the framework is given. In addition, in this paper we develop the ability to regulate the average walking rate of the biped to a continuum of values by modification of within-stride and stride-boundary characteristics, such as step length.
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paper_title: Configuring of Spiking Central Pattern Generator Networks for Bipedal Walking Using Genetic Algorthms

paper_content:

In limbed animals, spinal neural circuits responsible for controlling muscular activities during walking are called central pattern generators (CPG). CPG networks display oscillatory activities that actuates individual or groups of muscles in a coordinated fashion so that the limbs of the animal are flexed and extended at the appropriate time and with the required velocity for the animal to efficiently traverse various types of terrain, and to recover from environmental perturbation. Typically, the CPG networks are constructed with many neurons, each of which has a number of control parameters. As the number of muscles increases, it is often impossible to manually, albeit intelligently, select the network parameters for a particular movement. Furthermore, it is virtually impossible to reconfigure the parameters on-line. This paper describes how genetic algorithms (GA) can be used for on-line (re)configuring of CPG networks for a bipedal robot. We show that the neuron parameters and connection weights/network topology of a canonical walking network can be reconfigured within a few of generations of the GA. The networks, constructed with integrate-and-fire-with-adaptation (IFA) neurons, are implemented with a microcontroller and can be reconfigured to vary walking speed from 0.5Hz to 3.5Hz. The phase relationship between the hips and knees can be arbitrarily set (to within 1 degree) and prescribed complex joint angle profiles are realized. This is a powerful approach to generating complex muscle synergies for robots with multiple joints and distributed actuators.
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paper_title: Programmable central pattern generators: an application to biped locomotion control

paper_content:

We present a system of coupled nonlinear oscillators to be used as programmable central pattern generators, and apply it to control the locomotion of a humanoid robot. Central pattern generators are biological neural networks that can produce coordinated multidimensional rhythmic signals, under the control of simple input signals. They are found both in vertebrate and invertebrate animals for the control of locomotion. In this article, we present a novel system composed of coupled adaptive nonlinear oscillators that can learn arbitrary rhythmic signals in a supervised learning framework. Using adaptive rules implemented as differential equations, parameters such as intrinsic frequencies, amplitudes, and coupling weights are automatically adjusted to replicate a teaching signal. Once the teaching signal is removed, the trajectories remain embedded as the limit cycle of the dynamical system. An interesting aspect of this approach is that the learning is completely embedded into the dynamical system, and does not require external optimization algorithms. We use our system to encapsulate rhythmic trajectories for biped locomotion with a simulated humanoid robot, and demonstrate how it can be used to do online trajectory generation. The system can modulate the speed of locomotion, and even allow the reversal of direction (i.e. walking backwards). The integration of sensory feedback allows the online modulation of trajectories such as to increase the basin of stability of the gaits, and therefore the range of speeds that can be produced
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paper_title: On Ballistic Walking Locomotion of a Quadruped

paper_content:

This paper investigates ballistic motions in walking quadrupeds on a horizontal plane. The study is carried out on a quadruped consisting of a body and four identical two-link legs. Each leg has a knee joint and is connected to the body by a haunch joint. Three types of quadruped gaits, bound, amble, and trot, are studied. None of these gaits complies with a flight phase, but they all involve simultaneous and identical motion of two legs. Muscle activities are commonly believed to alternate with periods of relaxation. Our study, therefore, assumes that the swing phase is ballistic, i.e., no active control torque is exerted. Ballistic motion is achieved through appropriate initial velocities. These velocities result from impulsive active control torques and ground reactions exerted at the boundary instants of the single support phase. Natural ballistic motions are shown to exist for the three gaits and for each valid walking velocity class. Torque cost analysis shows that amble and trot gaits are more effi...
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paper_title: Automated evolutionary design, robustness, and adaptation of sidewinding locomotion of a simulated snake-like robot

paper_content:

Inspired by the efficient method of locomotion of the rattlesnake Crotalus cerastes, the objective of this work is automatic design through genetic programming (GP) of the fastest possible (sidewinding) locomotion of simulated limbless, wheelless snake-like robot (Snakebot). The realism of simulation is ensured by employing the Open Dynamics Engine (ODE), which facilitates implementation of all physical forces, resulting from the actuators, joints constrains, frictions, gravity, and collisions. Reduction of the search space of the GP is achieved by representation of Snakebot as a system comprising identical morphological segments and by automatic definition of code fragments, shared among (and expressing the correlation between) the evolved dynamics of the vertical and horizontal turning angles of the actuators of Snakebot. Empirically obtained results demonstrate the emergence of sidewinding locomotion from relatively simple motion patterns of morphological segments. Robustness of the sidewinding Snakebot, which is considered to be the ability to retain its velocity when situated in an unanticipated environment, is illustrated by the ease with which Snakebot overcomes various types of obstacles such as a pile of or burial under boxes, rugged terrain, and small walls. The ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics is discussed. Discovering compensatory locomotion traits, Snakebot recovers completely from single damage and recovers a major extent of its original velocity when more significant damage is inflicted. Exploring the opportunity for automatic design and adaptation of a simulated artifact, this work could be considered as a step toward building real Snakebots, which are able to perform robustly in difficult environments.
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paper_title: Neural networks for the control of a six-legged walking machine

paper_content:

Abstract In this paper a hierarchical control architecture for a six-legged walking machine is presented. The basic components of this architecture are neural networks which are taught in using examples of the control process. It is shown how the basic components “leg control” and “leg coordination” have been implemented by recurrent and feedforward networks respectively. The teaching process and the tests of the walking behaviour have mainly been done in a simulation system. First tests of the leg control on our real walking machine LAURON are also described.
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paper_title: Efficient Walking Speed Optimization of a Humanoid Robot

paper_content:

The development of optimized motions of humanoid robots that guarantee fast and also stable walking is an important task, especially in the context of autonomous soccer-playing robots in RoboCup. We present a walking motion optimization approach for the humanoid robot prototype HR18 which is equipped with a low-dimensional parameterized walking trajectory generator, joint motor controller and an internal stabilization. The robot is included as hardware-in-the-loop to define a low-dimensional black-box optimization problem. In contrast to previously performed walking optimization approaches, we apply a sequential surrogate optimization approach using stochastic approximation of the underlying objective function and sequential quadratic programming to search for a fast and stable walking motion. This is done under the conditions that only a small number of physical walking experiments should have to be carried out during the online optimization process. For the identified walking motion for the considered 55 cm tall humanoid robot, we measured a forward walking speed of more than 30 cm s-1 . With a modified version of the robot, even more than 40 cm s-1 could be achieved in permanent operation.
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paper_title: Structural Evolution of Central Pattern Generators for Bipedal Walking in 3D Simulation

paper_content:

Anthropomorphic walking for a simulated bipedal robot has been realized by means of artificial evolution of central pattern generator (CPG) networks. The approach has been investigated through full rigid-body dynamics simulations in 3D of a bipedal robot with 14 degrees of freedom. The half-center CPG model has been used as an oscillator unit, with interconnection paths between oscillators undergoing structural modifications using a genetic algorithm. In addition, the connection weights in a feedback network of predefined structure were evolved. Furthermore, a supporting structure was added to the robot in order to guide the evolutionary process towards natural, human-like gaits. Subsequently, this structure was removed, and the ability of the best evolved controller to generate a bipedal gait without the help of the supporting structure was verified. Stable, natural gait patterns were obtained, with a maximum walking speed of around 0.9 m/s.
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paper_title: Optimal trajectory generation for a prismatic joint biped robot using genetic algorithms

paper_content:

Abstract In this paper, a prismatic joint biped robot trajectory planning method is proposed. The minimum consumed energy is used as a criterion for trajectory generation, by using a real number genetic algorithm as an optimization tool. The minimum torque change cost function and constant vertical position trajectories are used in order to compare the results and verify the effectiveness of this method. The minimum consumed energy walking is stable and the impact of the foot with the ground is very small. Experimental investigations of a prismatic joint biped robot confirmed the predictions concerning the consumed energy and stability.
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paper_title: Generation of free gait-a graph search approach

paper_content:

A method is presented for the generation of a free gait for the straight-line motion of a quadruped walking machine. It uses a heuristic graph search procedure based on the A* algorithm. The method essentially looks into the consequences of a move to a certain depth before actually committing to it. Deadlocks and inefficiencies are thus sensed well in advance and avoided. >
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paper_title: Passive compliant quadruped robot using Central Pattern Generators for locomotion control

paper_content:

We present a new quadruped robot, ldquoCheetahrdquo, featuring three-segment pantographic legs with passive compliant knee joints. Each leg has two degrees of freedom - knee and hip joint can be actuated using proximal mounted RC servo motors, force transmission to the knee is achieved by means of a bowden cable mechanism. Simple electronics to command the actuators from a desktop computer have been designed in order to test the robot. A Central Pattern Generator (CPG) network has been implemented to generate different gaits. A parameter space search was performed and tested on the robot to optimize forward velocity.
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paper_title: Stability Analysis of Legged Locomotion Models by Symmetry-Factored Return Maps

paper_content:

We present a new stability analysis for hybrid legged locomotion systems based on the “symmetric” factorization of return maps. We apply this analysis to two-degrees-of-freedom (2DoF) and three-degrees-of-freedom (3DoF) models of the spring loaded inverted pendulum (SLIP) with different leg recirculation strategies. Despite the non-integrability of the SLIP dynamics, we obtain a necessary condition for asymptotic stability (and a sufficient condition for instability) at a fixed point, formulated as an exact algebraic expression in the physical parameters. We use this expression to characterize analytically the sensory cost and stabilizing benefit of various feedback schemes previously proposed for the 2DoF SLIP model, posited as a low-dimensional representation of running. We apply the result as well to a 3DoF SLIP model that will be treated at greater length in a companion paper as a descriptive model for the robot RHex.
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paper_title: Research on gait planning of artificial leg based on central pattern generator

paper_content:

Biped robot with heterogeneous legs (BRHL) is a novel robot model, which consists of an artificial leg and an intelligent bionic leg. The artificial leg is used to simulate the amputeepsilas healthy leg and the bionic leg works as the intelligent artificial limb. To describe the present gait of the healthy leg and make intelligent bionic leg follow the walking of artificial leg in all phases is the target of BRHLpsilas research. So gait planning of artificial leg is the emphasis of BRHLpsilas research. This paper uses the model of central pattern generator (CPG) in the research of artificial legpsilas gait planning from the point of biology. To obtain natural and robust walking pattern, genetic algorithm is used to optimize parameters of the CPG network model and the fitness function is formulated based on zero moment point (ZMP). Simulation results testify the feasibility of this method.
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paper_title: Evolution of Central Pattern Generators for Bipedal Walking in a Real-Time Physics Environment

paper_content:

We describe an evolutionary approach to the control problem of bipedal walking. Using a full rigid-body simulation of a biped, it was possible to evolve recurrent neural networks that controlled stable straight-line walking on a planar surface. No proprioceptive information was necessary in order to achieve this task. Furthermore, simple sensory input to locate a sound source was integrated to achieve directional walking. To our knowledge, this is the first work that demonstrates the application of evolutionary optimization to 3D physically simulated biped locomotion.
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paper_title: Slip-adaptive walk of quadruped robot

paper_content:

Abstract In this paper, we investigated the effects of the friction condition on walking pattern and energy efficiency, and based on the results, we proposed two new “slip-adaptive” strategies for generating a slip-adaptive walk. The first strategy for a slip-adaptive walk uses a slip reflex via a Central Pattern Generator (CPG) to change the walking pattern. The second strategy for a walk uses a force control to immediately compensate a slip. Using these strategies, a walk, which is adaptive to varying friction conditions and slips, becomes possible. The validity of the proposed method is confirmed through simulation and experimentation.
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paper_title: Autonomous evolution of dynamic gaits with two quadruped robots

paper_content:

A challenging task that must be accomplished for every legged robot is creating the walking and running behaviors needed for it to move. In this paper we describe our system for autonomously evolving dynamic gaits on two of Sony's quadruped robots. Our evolutionary algorithm runs on board the robot and uses the robot's sensors to compute the quality of a gait without assistance from the experimenter. First, we show the evolution of a pace and trot gait on the OPEN-R prototype robot. With the fastest gait, the robot moves at over 10 m/min, which is more than forty body-lengths/min. While these first gaits are somewhat sensitive to the robot and environment in which they are evolved, we then show the evolution of robust dynamic gaits, one of which is used on the ERS-110, the first consumer version of AIBO.
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paper_title: A movement pattern generator model using artificial neural networks

paper_content:

The authors have developed a movement pattern generator, using an artificial neural network (ANN) for generating periodic movement trajectories. This model is based on the concept of 'central pattern generators'. Jordan's (1986) sequential network, which is capable of learning sequences of patterns, was modified and used to generate several bipedal trajectories (or gaits), coded in task space, at different frequencies. The network model successfully learned all of the trajectories presented to it. The model has many attractive properties, such as limit cycle behavior, generalization of trajectories and frequencies, phase maintenance, and fault tolerance. The movement pattern generator model is potentially applicable for improved understanding of animal locomotion and for use in legged robots and rehabilitation medicine. >
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paper_title: Fuzzy-logic zero-moment-point trajectory generation for reduced trunk motions of biped robots

paper_content:

Trunk motions are typically used in biped robots to stabilize the locomotion. However, they can be very large for some leg trajectories unless they are carefully designed. This paper proposes a fuzzy-logic zero-moment-point (ZMP) trajectory generator that would eventually reduce the swing motion of the trunk significantly even though the leg trajectory is casually designed, for example, simply to avoid obstacles. The fuzzy-logic ZMP trajectory generator uses the leg trajectory as an input. The resulting ZMP trajectory is similar to that of a human one and continuously moves forward in the direction of the locomotion. The trajectory of the trunk to stabilize the locomotion is determined by solving a differential equation with the ZMP trajectory and the leg trajectory known. The proposed scheme is simulated on a 7-DOF biped robot in the sagittal plane. The simulation results show that the ZMP trajectory generated by the proposed fuzzy-logic generator increases the stability of the locomotion and thus reduces the motion range of the trunk significantly.
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paper_title: Humanoid robotics platforms developed in HRP

paper_content:

Abstract This paper presents humanoid robotics platform that consists of a humanoid robot and an open architecture software platform developed in METI’s Humanoid Robotics Project (HRP). The final version of the robot, called HRP-2, has 1540 mm height, 58 kg weight and 30 degrees of the freedom. The software platform includes a dynamics simulator and motion controllers of the robot for biped locomotion, falling and getting up motions. The platform has been used to develop various applications and is expected to initiate more humanoid robotics research.
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paper_title: A Parametric Optimization Approach to Walking Pattern Synthesis

paper_content:

Walking pattern synthesis is carried out using a spline-based parametric optimization technique. Generalized coordinates are approximated by spline functions of class C3fitted at knots uniformly distributed along the motion time. This high-order differentiability eliminates jerky variations of actuating torques. Through connecting conditions, spline polynomial coefficients are determined as a linear function of the joint coordinates at knots. These values are then dealt with as optimization parameters. An optimal control problem is formulated on the basis of a performance criterion to be minimized, representing an integral quadratic amount of driving torques. Using the above spline approximations, this primary problem is recast into a constrained non-linear optimization problem of mathematical programming, which is solved using a computing code implementing an SQP algorithm. As numerical simulations, complete gait cycles are generated for a seven-link planar biped. The only kinematic data to be accounted for are the walking speeds. Optimization of both phases of gait is carried out globally; it includes the optimization of transition configurations of the biped between successive phases of the gait cycle.
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paper_title: A universal stability criterion of the foot contact of legged robots - adios ZMP

paper_content:

This paper proposes a universal stability criterion of the foot contact of legged robots. The proposed method checks if the sum of the gravity and the inertia wrench applied to the COG of the robot, which is proposed to be the stability criterion, is inside the polyhedral convex cone of the contact wrench between the feet of a robot and its environment. The criterion can be used to determine the strong stability of the foot contact when a robot walks on an arbitrary terrain and/or when the hands of the robot are in contact with it under the sufficient friction assumption. The determination is equivalent to check if the ZMP is inside the support polygon of the feet when the robot walks on a horizontal plane with sufficient friction. The criterion can also be used to determine if the foot contact is sufficiently weakly stable when the friction follows a physical law. Therefore, the proposed criterion can be used to judge what the ZMP can, and it can be used in more universal cases
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paper_title: Bipedal locomotion control using a four-compartmental central pattern generator

paper_content:

In this paper, we develop a simple bipedal locomotion algorithm based on biological concepts. The algorithm utilized a central pattern generator (CPG) composed of four coupled neural oscillators (NO) to generate control signal for the bipedal robot. Feedbacks from the robot dynamics and the environment are used to update the CPG online. Our algorithm is then tested on a seven link model of bipedal robot. Simulation results suggest the proposed CPG can generate a smooth and continuous walking pattern for the robot.
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paper_title: Online Generation of Cyclic Leg Trajectories Synchronized with Sensor Measurement

paper_content:

The generation of trajectories for a biped robot is a problem which has been largely studied for several years, and many satisfying offline solutions exist for steady-state walking in absence of disturbances. The question is a little more complex when the generation of the desired trajectories of joints or links has to be achieved or adapted online, i.e. in real time, for example when it is wished to strongly synchronize these trajectories with an external motion. This is precisely the problem addressed in this paper. Indeed, we consider the case where the ''master'' motion is measured by a position sensor embedded on a human leg. We propose a method to synchronize the motion of a robot or of other device with respect to the output signal of the sensor. The main goal is to estimate as accurately as possible the current phase along the gait cycle. We use for that purpose a model based on a nonlinear oscillator, which we associate an observer. Introducing the sensor output in the observer allows us to compute the oscillator phase and to generate a synchronized multilinks trajectory, at a very low computational cost. The paper also presents evaluation results in terms of robustness against parameter estimation errors and velocity changes in the input.
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paper_title: A Learning Architecture Based on Reinforcement Learning for Adaptive Control of the Walking Machine LAURON

paper_content:

The learning of complex control behaviour of autonomous mobile robots is one of the actual research topics. In this article an intelligent control architecture is presented which integrates learning methods and available domain knowledge. This control architecture is based on Reinforcement Learning and allows continuous input and output parameters, hierarchical learning, multiple goals, self-organized topology of the used networks and online learning. As a testbed this architecture is applied to the six-legged walking machine LAURON to learn leg control and leg coordination.
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paper_title: Soccer playing humanoid robots: Processing architecture, gait generation and vision system

paper_content:

Research on humanoid robotics in Mechatronics and Automation (MA) Laboratory, Electrical and Computer Engineering (ECE), National University of Singapore (NUS) was started at the beginning of this decade. Various research prototypes for humanoid robots have been designed and are going through evolution over these years. These humanoids have been successfully participating in various robotic soccer competitions. In this paper, three major research and development aspects of the above humanoid research are discussed. The paper focuses on various practical and theoretical considerations involved in processing architecture, gait generation and vision systems.
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paper_title: Design of a Central Pattern Generator Using Reservoir Computing for Learning Human Motion

paper_content:

To generate coordinated periodic movements, robot locomotion demands mechanisms which are able to learn and produce stable rhythmic motion in a controllable way. Because systems based on biological central pattern generators (CPGs) can cope with these demands, these kind of systems are gaining in success. In this work we introduce a novel methodology that uses the dynamics of a randomly connected recurrent neural network for the design of CPGs. When a randomly connected recurrent neural network is excited with one or more useful signals, an output can be trained by learning an instantaneous linear mapping of the neuron states. This technique is known as reservoir computing (RC). We will show that RC has the necessary capabilities to be fruitful in designing a CPG that is able to learn human motion which is applicable for imitation learning in humanoid robots.
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paper_title: Real time gait generation for autonomous humanoid robots: A case study for walking

paper_content:

Abstract As autonomous humanoid robots assume more important roles in everyday life, they are expected to perform many different tasks and quickly adapt to unknown environments. Therefore, humanoid robots must generate quickly the appropriate gait based on information received from visual system. In this work, we present a new method for real time gait generation during walking based on Neural Networks. The minimum consumed energy gaits similar with human motion, are used to teach the Neural Network. After supervised learning, the Neural Network can quickly generate the humanoid robot gait. Simulation and experimental results utilizing the “Bonten-Maru I” humanoid robot show good performance of the proposed method.
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paper_title: Dynamic balance of a biped robot using fuzzy reinforcement learning agents

paper_content:

This paper presents a general fuzzy reinforcement learning (FRL) method for biped dynamic balance control. Based on a neuro fuzzy network architecture, different kinds of expert knowledge and measurement-based information can be incorporated into the FRL agent to initialise its action network, critic network and/or evaluation feedback module so as to aecelerate its learning. The proposed FRL agent is constructed and verified using the simulation model of a physical biped robot. The sinmtation analysis shows that by incorporation of the human intuitive balancing knowledge and walking evaluation knowledge, the FRL agent's learning rate for side-to-side and front-to-back balance of the simulated biped can be improved. We also demonstrate that it is possible for a biped robot to start its walking with a priori knowledge and then learn to improve its behaviour with the FRL agents.
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paper_title: Development of Adaptive Modular Active Leg (AMAL) using bipedal robotics technology

paper_content:

The objective of the work presented here is to develop a low cost active above knee prosthetic device exploiting bipedal robotics technology which will work utilizing the available biological motor control circuit properly integrated with a Central Pattern Generator (CPG) based control scheme. The approach is completely different from the existing Active Prosthetic devices, designed primarily as standalone systems utilizing multiple sensors and embedded rigid control schemes. In this research, first we designed a fuzzy logic based methodology for offering suitable gait pattern for an amputee, followed by formulating a suitable algorithm for designing a CPG, based on Rayleigh's oscillator. An indigenous probe, Humanoid Gait Oscillator Detector (HGOD) has been designed for capturing gait patterns from various individuals of different height, weight and age. These data are used to design a Fuzzy inference system which generates most suitable gait pattern for an amputee. The output of the Fuzzy inference system is used for designing a CPG best suitable for the amputee. We then developed a CPG based control scheme for calculating the damping profile in real time for maneuvering a prosthetic device called AMAL (Adaptive Modular Active Leg). Also a number of simulation results are presented which show the stable behavior of knee and hip angles and determine the stable limit cycles of the network.
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paper_title: Fast Biped Walking with a Sensor-driven Neuronal Controller and Real-time Online Learning

paper_content:

In this paper, we present our design and experiments on a planar biped robot under the control of a pure sensor-driven controller. This design has some special mechanical features, for example small curved feet allowing rolling action and a properly positioned center of mass, that facilitate fast walking through exploitation of the robot's natural dynamics. Our sensor-driven controller is built with biologically inspired sensor- and motor-neuron models, and does not employ any kind of position or trajectory tracking control algorithm. Instead, it allows our biped robot to exploit its own natural dynamics during critical stages of its walking gait cycle. Due to the interaction between the sensor-driven neuronal controller and the properly designed mechanics of the robot, the biped robot can realize stable dynamic walking gaits in a large domain of the neuronal parameters. In addition, this structure allows the use of a policy gradient reinforcement learning algorithm to tune the parameters of the sensor-driven controller in real-time, during walking. This way RunBot can reach a relative speed of 3.5 leg lengths per second after only a few minutes of online learning, which is faster than that of any other biped robot, and is also comparable to the fastest relative speed of human walking.
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paper_title: Design of central pattern generator for humanoid robot walking based on multi-objective GA

paper_content:

Recently, the field of humanoid robotics attracts more and more interest and the research on humanoid locomotion based on central pattern generators (CPG) reveals many challenging aspects. This paper describes the design of CPG for stable humanoid bipedal locomotion using an evolutionary approach. In this research, each joint of the humanoid is driven by a neuron that consists of two coupled neural oscillators, and corresponding joint's neurons are connected by strength weight. To achieve natural and robust walking pattern, an evolutionary-based multi-objective optimization algorithm is used to solve the weight optimization problem. The fitness functions are formulated based on zero moment point (ZMP), global attitude of the robot and the walking speed. In the algorithms, real value coding and tournament selection are applied, the crossover and mutation operators are chosen as heuristic crossover and boundary mutation respectively. Following evolving, the robot is able to walking in the given environment and a simulation shows the result.
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paper_title: Pattern generators with sensory feedback for the control of quadruped locomotion

paper_content:

Central pattern generators (CPGs) are becoming a popular model for the control of locomotion of legged robots. Biological CPGs are neural networks responsible for the generation of rhythmic movements, especially locomotion. In robotics, a systematic way of designing such CPGs as artificial neural networks or systems of coupled oscillators with sensory feedback inclusion is still missing. In this contribution, we present a way of designing CPGs with coupled oscillators in which we can independently control the ascending and descending phases of the oscillations (i.e. the swing and stance phases of the limbs). Using insights from dynamical system theory, we construct generic networks of oscillators able to generate several gaits under simple parameter changes. Then we introduce a systematic way of adding sensory feedback from touch sensors in the CPG such that the controller is strongly coupled with the mechanical system it controls. Finally we control three different simulated robots (iCub, Aibo and Ghostdog) using the same controller to show the effectiveness of the approach. Our simulations prove the importance of independent control of swing and stance duration. The strong mutual coupling between the CPG and the robot allows for more robust locomotion, even under non precise parameters and non-flat environment.
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paper_title: Evolving Dynamical Neural Networks for Adaptive Behavior

paper_content:

We would like the behavior of the artificial agents that we construct to be as well-adapted to their environments as natural animals are to theirs. Unfortunately, designing controllers with these properties is a very difficult task. In this article, we demonstrate that continuous-time recurrent neural networks are a viable mechanism for adaptive agent control and that the genetic algorithm can be used to evolve effective neural controllers. A significant advantage of this approach is that one need specify only a measure of an agent's overall performance rather than the precise motor output trajectories by which it is achieved. By manipulating the performance evaluation, one can place selective pressure on the development of controllers with desired properties. Several novel controllers have been evolved, including a chemotaxis controller that switches between different strategies depending on environmental conditions, and a locomotion controller that takes advantage of sensory feedback if available but th...
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paper_title: Optimal Path and Gait Generations Simultaneously of a Six-legged Robot Using a GA-Fuzzy Approach

paper_content:

This paper describes a new method for generating optimal path and gait simultaneously of a six-legged robot using a combined GA-fuzzy approach. The problem of combined path and gait generations involves three steps, namely determination of vehicle’s trajectory, foothold selection and design of a sequence of leg movements. It is a complicated task and no single traditional approach is found to be successful in handling this problem. Moreover, the traditional approaches do not consider optimization issues, yet they are computationally expensive. Thus, the generated path and gaits may not be optimal in any sense. To solve such problems optimally, there is still a need for the development of an efficient and computationally faster algorithm. In the proposed genetic-fuzzy approach, optimal path and gaits are generated by using fuzzy logic controllers (FLCs) and genetic algorithms (GAs) are used to find optimized FLCs. The optimization is done off-line on a number of training scenarios and optimal FLCs are found. The hexapod can then use these GA-tuned FLCs to navigate in test-case scenarios.
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paper_title: Efference copies in neural control of dynamic biped walking

paper_content:

In the early 1950s, von Holst and Mittelstaedt proposed that motor commands copied within the central nervous system (efference copy) help to distinguish 'reafference' activity (afference activity due to self-generated motion) from 'exafference' activity (afference activity due to external stimulus). In addition, an efference copy can be also used to compare it with the actual sensory feedback in order to suppress self-generated sensations. Based on these biological findings, we conduct here two experimental studies on our biped ''RunBot'' where such principles together with neural forward models are applied to RunBot's dynamic locomotion control. The main purpose of this article is to present the modular design of RunBot's control architecture and discuss how the inherent dynamic properties of the different modules lead to the required signal processing. We believe that the experimental studies pursued here will sharpen our understanding of how the efference copies influence dynamic locomotion control to the benefit of modern neural control strategies in robots.
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paper_title: Neuroethological Concepts and their Transfer to Walking Machines

paper_content:

A systems approach to animal motor behavior reveals concepts that can be useful for the pragmatic design of walking machines. This is because the relation of animal behavior to its underlying nervous control algorithms bears many parallels to the relation of machine function to electronic control. Here, three major neuroethological concepts of motor behavior are described in terms of a conceptual framework based on artificial neural networks (ANN). Central patterns of activity and postural reflexes are both interpreted as a result of feedback loops, with the distinction of loops via an internal model from loops via the physical environment (body, external world). This view allows continuous transitions between predictive (centrally driven) and reactive (reflex driven) motor systems. Motor primitives, behavioral modules that are elicited by distinct commands, are also considered. ANNs capture these three major concepts in terms of a formal description, in which the interactions and mutual interdependences ...
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paper_title: Gait Optimization through Search

paper_content:

We present a search-based method for the generation of a terrain-adaptive optimal gait of a six-legged walking machine. In this, several heuristic rules have been proposed to reduce the search effort. We identify the useful support states of the machine and form a table to indicate for each of these states the list of other states to which a transition can be made. This helps in converging to and maintaining a periodic gait through a limited search while retaining adequate options to deviate from such a gait as and when needed. The criterion for optimization is coded into a function that evaluates the promise of a node in the search graph. We have shown how this function may be designed to generate the common periodic gaits like the wave gait, the equal phase gait, and the follow-the-leader gait. The purpose is to demonstrate that the proposed method is sufficiently general and can cater to a wide range of optimizing requirements.
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paper_title: Robustness of the dynamic walk of a biped robot subjected to disturbing external forces by using CMAC neural networks

paper_content:

Abstract In this paper, we propose a control strategy allowing us to perform the dynamic walking gait of an under-actuated robot even if this one is subjected to destabilizing external disturbances. This control strategy is based on two stages. The first one consists of using a set of pragmatic rules in order to generate a succession of passive and active phases allowing us to perform a dynamic walking gait of the robot. The joint trajectories of this reference gait are learned by using neural networks. In the second stage, we use these neural networks to generate the learned trajectories during the first stage. The goal of the use of these neural networks is to increase the robustness of the control of the dynamic walking gait of this robot in the case of external disturbances. The first experimental results are also presented.
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paper_title: A Control Approach for Actuated Dynamic Walking in Biped Robots

paper_content:

This paper presents an approach for the closed-loop control of a fully actuated biped robot that leverages its natural dynamics when walking. Rather than prescribing kinematic trajectories, the approach proposes a set of state-dependent torques, each of which can be constructed from a combination of low-gain spring-damper couples. Accordingly, the limb motion is determined by interaction of the passive control elements and the natural dynamics of the biped, rather than being dictated by a reference trajectory. In order to implement the proposed approach, the authors develop a model-based transformation from the control torques that are defined in a mixed reference frame to the actuator joint torques. The proposed approach is implemented in simulation on an anthropomorphic biped. The simulated biped is shown to converge to a stable, natural-looking walk from a variety of initial configurations. Based on these simulations, the mechanical cost of transport is computed and shown to be significantly lower than that of trajectory-tracking approaches to biped control, thus validating the ability of the proposed idea to provide efficient dynamic walking. Simulations further demonstrate walking at varying speeds and on varying ground slopes. Finally, controller robustness is demonstrated with respect to forward and backward push-type disturbances and with respect to uncertainty in model parameters.
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paper_title: On-line stable gait generation of a two-legged robot using a genetic–fuzzy system

paper_content:

Abstract Gait generation for legged vehicles has since long been considered as an area of keen interest by the researchers. Soft computing is an emerging technique, whose utility is more stressed, when the problems are ill-defined, difficult to model and exhibit large scale solution spaces. Gait generation for legged vehicles is a complex task. Therefore, soft computing can be applied to solve it. In this work, gait generation problem of a two-legged robot is modeled using a fuzzy logic controller (FLC), whose rule base is optimized offline, using a genetic algorithm (GA). Two different GA-based approaches (to improve the performance of FLC) are developed and their performances are compared to that of manually constructed FLC. Once optimized, the FLCs will be able to generate dynamically stable gait of the biped. As the CPU-time of the algorithm is found to be only 0.002 s in a P-III PC, the algorithm is suitable for on-line (real-time) implementations.
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paper_title: Dynamic walking and running of a bipedal robot using hybrid central pattern generator method

paper_content:

This paper presents simulation and experimental results of dynamic walking and running of a bipedal robot. We proposed the hybrid central pattern generator (H-CPG) method to realize adaptive dynamic motions including stepping and jumping. This method basically consisted of CPG models, but was added the force control system that controlled the acting force from a leg to a floor in the vertical and the horizontal directions separately. At this moment, 2D walking and running was realized on level ground and a slope with a speed of 1.6 m/s.
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paper_title: Biped dynamic walking using reinforcement learning

paper_content:

This paper presents some results from a study of biped dynamic walking using reinforcement learning. During this study a hardware biped robot was built, a new reinforcement learning algorithm as well as a new learning architecture were developed. The biped learned dynamic walking without any previous knowledge about its dynamic model. The self scaling reinforcement (SSR) learning algorithm was developed in order to deal with the problem of reinforcement learning in continuous action domains. The learning architecture was developed in order to solve complex control problems. It uses different modules that consist of simple controllers and small neural networks. The architecture allows for easy incorporation of new modules that represent new knowledge, or new requirements for the desired task.
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paper_title: Locomotion pattern generation of semi-looper type robots using central pattern generators based on van der Pol oscillators

paper_content:

A control problem is studied for generating the locomotion pattern of a semi-looper type robot by applying central pattern generators (CPGs), in which such a robot can realize two-rhythm motion and green caterpillar locomotion depending on the condition of environment. After deriving the dynamical model with two links and one actuator, the simulation of the robot is conducted using a CPG consisting of one van der Pol (VDP) oscillator. A CPG network composed of two VDP oscillators is further constructed to realize four-rhythm motion with three links and two actuators.
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paper_title: A pattern generator of humanoid robots walking on a rough terrain using a handrail

paper_content:

This paper presents a motion pattern generator of humanoid robots that walks on a flat plane, steps and a rough terrain. It is guaranteed rigorously that the desired contact between a humanoid robot and terrain should be maintained by keeping the contact wrench sum between them inside the contact wrench cone under the sufficient friction assumption. A walking pattern is generated by solving the contact wrench equations and by applying the resolved momentum control.
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paper_title: Dynamics and balance of a humanoid robot during manipulation tasks

paper_content:

In this paper, we analyze the balance of a humanoid robot during manipulation tasks. By defining the generalized zero-moment point (GZMP), we obtain the region of it for keeping the balance of the robot during manipulation. During manipulation, the convex hull of the supporting points forms the 3-D convex polyhedron. The region of the GZMP is obtained by considering the infinitesimal displacement and the moment about the edges of the convex hull. We show that we can determine whether or not the robot may keep balance for several styles of manipulation tasks, such as pushing and pulling an object. The effectiveness of our proposed method is demonstrated by simulation.
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paper_title: Dynamically balanced optimal gaits of a ditch-crossing biped robot

paper_content:

This paper deals with the generation of dynamically balanced gaits of a ditch-crossing biped robot having seven degrees of freedom (DOFs). Three different approaches, namely analytical, neural network (NN)-based and fuzzy logic (FL)-based, have been developed to solve the said problem. The former deals with the analytical modeling of the ditch-crossing gait of a biped robot, whereas the latter two approaches aim to maximize the dynamic balance margin of the robot and minimize the power consumption during locomotion, after satisfying a constraint stating that the changes of joint torques should lie within a pre-specified value to ensure its smooth walking. It is to be noted that the power consumption and dynamic balance of the robot are also dependent on the position of the masses on various links and the trajectory followed by the hip joint. A genetic algorithm (GA) is used to provide training off-line, to the NN-based and FL-based gait planners developed. Once optimized, the planners will be able to generate the optimal gaits on-line. Both the NN-based and FL-based gait planners are able to generate more balanced gaits and that, too, at the cost of lower power consumption compared to those yielded by the analytical approach. The NN-based and FL-based approaches are found to be more adaptive compared to the other approach in generating the gaits of the biped robot.
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paper_title: An effective trajectory generation method for bipedal walking

paper_content:

This paper presents the virtual height inverted pendulum mode (VHIPM), which is a simple and effective trajectory generation method for the stable walking of biped robots. VHIPM, which is based on the inverted pendulum mode (IPM), can significantly reduce the zero moment point (ZMP) error by adjusting the height in the inverted pendulum. We show the relationship between VHIPM and other popular trajectory generation methods, and compare the ZMP errors in walking when trajectories are generated by various methods including VHIPM. We also investigate the sensitivity of the ZMP error in VHIPM to the step length, walking period and mass distribution of a robot. The simulation results show that VHIPM significantly reduces the ZMP errors compared to other methods under various circumstances.
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paper_title: Landing Force Control for Humanoid Robot by Time-Domain Passivity Approach

paper_content:

This paper proposes a control method to absorb the landing force or the ground reaction force for a stable dynamic walking of a humanoid robot. Humanoid robot may become unstable during walking due to the impulsive contact force of the sudden landing of its foot. Therefore, a control method to decrease the landing force is required. In this paper, time-domain passivity control approach is applied for this purpose. Ground and the foot of the robot are modeled as two one-port network systems that are connected, and exchange energy with each other. The time-domain passivity controller with admittance causality is implemented, which has the landing force as input and foot's position to trim off the force as output. The proposed landing force controller can enhance the stability of the walking robot from simple computation. The small-sized humanoid robot, HanSaRam-VII that has 27 DOFs, is developed to verify the proposed scheme through dynamic walking experiments.
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paper_title: OpenHRP: Open Architecture Humanoid Robotics Platform

paper_content:

This paper introduces an open architecture humanoid robotics platform (OpenHRP for short) on which various building blocks of humanoid robotics can be investigated. OpenHRP is a virtual humanoid robot platform with a compatible humanoid robot, and consists of a simulator of humanoid robots and motion control library for them which can also be applied to a compatible humanoid robot as it is. OpenHRP also has a view simulator of humanoid robots on which humanoid robot vision can be studied. The consistency between the simulator and the robot are enhanced by introducing a new algorithm to simulate repulsive force and torque between contacting objects. OpenHRP is expected to initiate the exploration of humanoid robotics on an open architecture software and hardware, thanks to the unification of the controllers and the examined consistency between the simulator and a real humanoid robot.
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paper_title: Comparison of different gaits with rotation of the feet for a planar biped

paper_content:

Fast human walking includes a phase where the stance heel rises from the ground and the stance foot rotates about the stance toe. This phase where the biped becomes under-actuated is not present during the walk of humanoid robots. The objective of this study is to determine if this phase is useful to reduce the energy consumed in the walking. In order to study the efficiency of this phase, six cyclic gaits are presented for a planar biped robot. The simplest cyclic motion is composed of successive single support phases with flat stance foot on the ground. The most complex cyclic motion is composed of single support phases that include a sub-phase of rotation of the stance foot about the toe and of finite time double support phase. For the synthesis of these walking gaits, optimal motions with respect to the torque cost, are defined by taking into account given performances of actuators. It is shown that for fast motions a foot rotation sub-phase is useful to reduce the criteria cost. In the optimization process, under-actuated phase (foot rotation phase), fully-actuated phase (flat foot phase) and over-actuated phase (double support phase) are considered.
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paper_title: Modifiable Walking Pattern of a Humanoid Robot by Using Allowable ZMP Variation

paper_content:

In order to handle complex navigational commands, this paper proposes a novel algorithm that can modify a walking period and a step length in both sagittal and lateral planes. By allowing a variation of zero moment point (ZMP) over the convex hull of foot polygon, it is possible to change the center of mass (CM) position and velocity independently throughout the single support phase. This permits a range of dynamic walking motion, which is not achievable using the 3-D linear inverted pendulum mode (3D-LIPM). In addition, the proposed algorithm enables to determine the dynamic feasibility of desired motion via the construction of feasible region, which is explicitly computed from the current CM state with simple ZMP functions. Moreover, adopting the closed-form functions makes it possible to calculate the algorithm in real time. The effectiveness of the proposed algorithm is demonstrated through both computer simulation and experiment on the humanoid robot, HanSaRam-VII, developed at the Robot Intelligence Technology (RIT) laboratory, Korea Advanced Institute of Science and Technology (KAIST).
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paper_title: Experimental Validation of a Framework for the Design of Controllers that Induce Stable Walking in Planar Bipeds

paper_content:

In this paper we present the experimental validation of a framework for the systematic design, analysis, and performance enhancement of controllers that induce stable walking in N -link underactuated planar biped robots. Controllers designed via this framework act by enforcing virtual constraints—holonomic constraints imposed via feedback—on the robot’s configuration, which create an attracting two-dimensional invariant set in the full walking model’s state space. Stability properties of resulting walking motions are easily analyzed in terms of a two-dimensional subdynamic of the full walking model. A practical introduction to and interpretation of the framework is given. In addition, in this paper we develop the ability to regulate the average walking rate of the biped to a continuum of values by modification of within-stride and stride-boundary characteristics, such as step length.
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paper_title: Biped gait generation and control based on a unified property of passive dynamic walking

paper_content:

Principal mechanisms of passive dynamic walking are studied from the mechanical energy point of view, and novel gait generation and control methods based on passive dynamic walking are proposed. First, a unified property of passive dynamic walking is derived, which shows that the walking system's mechanical energy increases proportionally with respect to the position of the system's center of mass. This yields an interesting indeterminate equation that determines the relation between the system's control torques and its center of mass. By solving this indeterminate equation for the control torque, active dynamic walking on a level can then be realized. In addition, the applications to the robust energy referenced control are discussed. The effectiveness and control performances of the proposed methods have been investigated through numerical simulations.
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paper_title: Self-Excited Walking of a Biped Mechanism

paper_content:

The authors studied the self-excited walking of a four-link biped mechanism that possesses an actuated hip joint and passive knee joints with stoppers. They showed that the self-excitation control enables the three-degree-of-freedom planar biped model to walk on level ground by numerical simulation. From the parameter study, it was found that stable walking locomotion is possible over a wide range of feedback gain and link parameter values and that the walking period is almost independent of the feedback gain. Various characteristics of the self-excited walking of a biped mechanism were examined in relation to leg length, and length and mass ratios of the shank. Next, a biped mechanism was manufactured similar to the analytical model. After parameter modification, the authors demonstrated that the biped robot can perform natural dynamic walking on a plane with a 0.8 degree inclination. The simulated results also agree with the experimental walking locomotion.
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paper_title: Real-time humanoid motion generation through ZMP manipulation based on inverted pendulum control

paper_content:

A humanoid robot is expected to be a rational form of machine to act in the real human environment and support people through interaction with them. Current humanoid robots, however, lack in adaptability, agility, or high-mobility enough to meet the expectations. In order to enhance high-mobility, the humanoid motion should be generated in real-time in accordance with the dynamics, which commonly requires a large amount of computation and has not been implemented so far. We have developed a real-time motion generation method that controls the center of gravity (COG) by indirect manipulation of the zero moment point (ZMP). The real-time response of the method provides humanoid robots with high-mobility. In the paper, the algorithm is presented. It consists of four parts, namely, the referential ZMP planning, the ZMP manipulation, the COG velocity decomposition to joint angles, and local control of joint angles. An advantage of the algorithm lies in its applicability to humanoids with a lot of degrees of freedom. The effectiveness of the proposed method is verified by computer simulations.
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paper_title: A Pattern Generator of Humanoid Robots Walking on a Rough Terrain

paper_content:

This paper presents a biped humanoid robot that is able to walk on a rough terrain while touching a handrail. The contact wrench sum (CWS for short) is used as the criterion to judge if the contact between the robot and the environment is strongly stable under the sufficient friction assumption, where the contact points are not coplanar and the normal vectors at the points are not identical. It is confirmed that the proposed pattern generator can make the robot walk as desired in dynamics simulations and experiments, and the motions can be improved by a hand position control and using waist joints.
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paper_title: Hardware design and gait generation of humanoid soccer robot Stepper-3D

paper_content:

This paper presents the hardware design and gait generation of humanoid soccer robot Stepper-3D. Virtual Slope Walking, inspired by Passive Dynamic Walking, is introduced for gait generation. In Virtual Slope Walking, by actively extending the stance leg and shortening the swing leg, the robot walks on level ground as it walks down a virtual slope. In practical, Virtual Slope Walking is generated by connecting three key frames in the sagittal plane with sinusoids. Aiming for improving the walking stability, the parallel double crank mechanism are adopted in the leg structure. Experimental results show that Stepper-3D achieves a fast forward walking speed of 0.5 m/s and accomplishes omnidirectional walking. Stepper-3D performed fast and stable walking in the RoboCup 2008 Humanoid competitions.
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paper_title: Bipedal Locomotion: Stopping and the Standing/Balance Gait

paper_content:

A bipedal locomotion system is synthesized to characterize some of the previously overlooked aspects of the locomotion process, specifically standing/balance and initiation and stopping. The locomotion system is described by a three-element three-dimensional model consisting of two lower limbs and an upper body. The system equations of motion are derived using variational methods, and are retained in their nonlinear form. The impulsive contact events of impact of the swing limb with the ground and transfer of support are incorporated into the model. Bipedal locomotion is synthesized through numerical simulations. Several control inputs are studied for establishing and sustaining the standing/balance gait. Subsequent motion is analyzed via phase-space portraits. It is shown that an impulsive torque is sufficient for establishing and controlling the standing/balance gait as well as steady locomotion.
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paper_title: Self-Excited Walking of a Biped Mechanism with Feet

paper_content:

In this paper we present a theoretical and experimental study of the self-excited walking of a biped mechanism with knees and feet on level ground. This biped mechanism possesses a single motor at the hip joint and active lock mechanisms at both knee joints. We first show that the self-excitation control enables the three-degrees-of-freedom planar biped model with feet to walk on level ground, and that a stable walking locomotion is possible over a wide range of feedback gain and a foot radius of up to 0.3 m. Subsequently, we describe the manufactured biped Robot 2 which is similar to the analytical model and we show that the biped robot can perform natural dynamic walking on a level floor. It is also shown that the simulated results agree well with the experimental results. We further describe the wireless-controlled biped Robot 3, which can walk on a level floor carrying batteries and electronic circuits inside the thighs.
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paper_title: Posture/Walking Control for Humanoid Robot Based on Kinematic Resolution of CoM Jacobian With Embedded Motion

paper_content:

This paper proposes the walking pattern generation method, the kinematic resolution method of center of mass (CoM) Jacobian with embedded motions, and the design method of posture/walking controller for humanoid robots. First, the walking pattern is generated using the simplified model for bipedal robot. Second, the kinematic resolution of CoM Jacobian with embedded motions makes a humanoid robot balanced automatically during movement of all other limbs. Actually, it offers an ability of whole body coordination to humanoid robot. Third, the posture/walking controller is completed by adding the CoM controller minus the zero moment point controller to the suggested kinematic resolution method. We prove that the proposed posture/walking controller brings the disturbance input-to-state stability for the simplified bipedal walking robot model. Finally, the effectiveness of the suggested posture/walking control method is shown through experiments with regard to the arm dancing and walking of humanoid robot.
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paper_title: Energy-Efficient and High-Speed Dynamic Biped Locomotion Based on Principle of Parametric Excitation

paper_content:

We clarified that the common necessary condition for generating a dynamic gait results from the requirement to restore mechanical energy through studies on passive dynamic walking mechanisms. This paper proposes a novel method of generating a dynamic gait that can be found in the mechanism of a swing inspired by the principle of parametric excitation using telescopic leg actuation. We first introduce a simple underactuated biped model with telescopic legs and semicircular feet and propose a law to control the telescopic leg motion. We found that a high-speed dynamic bipedal gait can easily be generated by only pumping the swing leg mass. We then conducted parametric studies by adjusting the control and physical parameters and determined how well the basic gait performed by introducing some performance indexes. Improvements in energy efficiency by using an elastic-element effect were also numerically investigated. Further, we theoretically proved that semicircular feet have a mechanism that decreases the energy dissipated by heel-strike collisions. We provide insights throughout this paper into how zero-moment-point-free robots can generate a novel biped gait.
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paper_title: Observer-based dynamic walking control for biped robots

paper_content:

This article presents a novel observer-based control system to achieve reactive motion generation for dynamic biped walking. The proposed approach combines a feedback controller with an online generated feet pattern to assure a stable gait. Using the desired speed of the robot, a preview control system derives the dynamics of the robot's body, and thereby the trajectory of its center of mass, to ensure a zero moment point (ZMP) movement, which results in a stable execution of the calculated step pattern. Extending the control system by an observer, based on this knowledge and the measured sensor values, compensates for errors in the model parameters and disturbances encountering while walking.
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paper_title: Measurement and comparison of humanoid H7 walking with human being

paper_content:

Abstract This paper describes our research efforts aimed at understanding human being walking functions. Using a motion-capture system, force plates and distributed force sensors, walk motion of both human being and humanoid H7 was captured. Experimental results are shown. Comparisons in between human being and H7 walk made using the following characteristics: (1) ZMP trajectories; (2) torso movement; (3) free leg trajectories; (4) joint angle usage; (5) joint torque usage. Furthermore, implications of the comparisons to the humanoid robot are discussed.
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paper_title: Fuzzy-logic zero-moment-point trajectory generation for reduced trunk motions of biped robots

paper_content:

Trunk motions are typically used in biped robots to stabilize the locomotion. However, they can be very large for some leg trajectories unless they are carefully designed. This paper proposes a fuzzy-logic zero-moment-point (ZMP) trajectory generator that would eventually reduce the swing motion of the trunk significantly even though the leg trajectory is casually designed, for example, simply to avoid obstacles. The fuzzy-logic ZMP trajectory generator uses the leg trajectory as an input. The resulting ZMP trajectory is similar to that of a human one and continuously moves forward in the direction of the locomotion. The trajectory of the trunk to stabilize the locomotion is determined by solving a differential equation with the ZMP trajectory and the leg trajectory known. The proposed scheme is simulated on a 7-DOF biped robot in the sagittal plane. The simulation results show that the ZMP trajectory generated by the proposed fuzzy-logic generator increases the stability of the locomotion and thus reduces the motion range of the trunk significantly.
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paper_title: Humanoid robotics platforms developed in HRP

paper_content:

Abstract This paper presents humanoid robotics platform that consists of a humanoid robot and an open architecture software platform developed in METI’s Humanoid Robotics Project (HRP). The final version of the robot, called HRP-2, has 1540 mm height, 58 kg weight and 30 degrees of the freedom. The software platform includes a dynamics simulator and motion controllers of the robot for biped locomotion, falling and getting up motions. The platform has been used to develop various applications and is expected to initiate more humanoid robotics research.
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paper_title: Recent progress and development of the humanoid robot HanSaRam

paper_content:

This paper presents an overview of the recent progress and development of the humanoid robot, HanSaRam series, which have been developed in the Robot Intelligence Technology (RIT) Laboratory, KAIST since 2000. The HanSaRam series have been designed and developed as a small-sized robot for researching walking gate generation, navigation, task planning and HuroCup of FIRA. In particular, the performance of the 7th and 8th versions have been remarkably improved in the aspect of walking pattern generation and task planning. This paper describes the overall design and architecture of recently developed two versions of HanSaRam along with a developed vision simulator tool and the real-time walking gate generation scheme, modifiable walking pattern generator.
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paper_title: Design and construction of a series of compact humanoid robots and development of biped walk control strategies

paper_content:

Abstract Design and construction of compact body humanoid robots and various biped locomotion control strategies implemented onto them in the ESYS humanoid project at the Engineering Systems Laboratory are presented. Design concepts and hardware specifications of the constructed compact size humanoid robots from Mk.1 to Mk.5 are discussed. As for the biped walk control, four biped locomotion control strategies all of which have various advantages such as versatility, high-energy efficiency, smooth loading on hardware, and real-time gait generation, are proposed. 3D biped dynamic walking of the constructed humanoids is realized by implementing the proposed biped control strategies onto them. Results of evaluation experiments on the proposed control strategies are reported.
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paper_title: A Parametric Optimization Approach to Walking Pattern Synthesis

paper_content:

Walking pattern synthesis is carried out using a spline-based parametric optimization technique. Generalized coordinates are approximated by spline functions of class C3fitted at knots uniformly distributed along the motion time. This high-order differentiability eliminates jerky variations of actuating torques. Through connecting conditions, spline polynomial coefficients are determined as a linear function of the joint coordinates at knots. These values are then dealt with as optimization parameters. An optimal control problem is formulated on the basis of a performance criterion to be minimized, representing an integral quadratic amount of driving torques. Using the above spline approximations, this primary problem is recast into a constrained non-linear optimization problem of mathematical programming, which is solved using a computing code implementing an SQP algorithm. As numerical simulations, complete gait cycles are generated for a seven-link planar biped. The only kinematic data to be accounted for are the walking speeds. Optimization of both phases of gait is carried out globally; it includes the optimization of transition configurations of the biped between successive phases of the gait cycle.
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paper_title: Programming full-body movements for humanoid robots by observation

paper_content:

Abstract The formulation and optimization of joint trajectories for humanoid robots is quite different from this same task for standard robots because of the complexity of humanoid robots’ kinematics and dynamics. In this paper we exploit the similarity between human motion and humanoid robot motion to generate joint trajectories for humanoids. In particular, we show how to transform human motion information captured by an optical tracking device into a high dimensional trajectory for a humanoid robot. We propose an automatic approach to relate humanoid robot kinematic parameters to the kinematic parameters of a human performer. Based on this relationship we infer the desired trajectories in robot joint space. B-spline wavelets are utilized to efficiently represent the trajectories. The density of the basis functions on the time axis is selected automatically. Large-scale optimization techniques are employed to solve the underlying computational problems efficiently. We applied our method to the task of teaching a humanoid robot how to make various naturally looking movements.
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paper_title: A universal stability criterion of the foot contact of legged robots - adios ZMP

paper_content:

This paper proposes a universal stability criterion of the foot contact of legged robots. The proposed method checks if the sum of the gravity and the inertia wrench applied to the COG of the robot, which is proposed to be the stability criterion, is inside the polyhedral convex cone of the contact wrench between the feet of a robot and its environment. The criterion can be used to determine the strong stability of the foot contact when a robot walks on an arbitrary terrain and/or when the hands of the robot are in contact with it under the sufficient friction assumption. The determination is equivalent to check if the ZMP is inside the support polygon of the feet when the robot walks on a horizontal plane with sufficient friction. The criterion can also be used to determine if the foot contact is sufficiently weakly stable when the friction follows a physical law. Therefore, the proposed criterion can be used to judge what the ZMP can, and it can be used in more universal cases
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paper_title: Soccer playing humanoid robots: Processing architecture, gait generation and vision system

paper_content:

Research on humanoid robotics in Mechatronics and Automation (MA) Laboratory, Electrical and Computer Engineering (ECE), National University of Singapore (NUS) was started at the beginning of this decade. Various research prototypes for humanoid robots have been designed and are going through evolution over these years. These humanoids have been successfully participating in various robotic soccer competitions. In this paper, three major research and development aspects of the above humanoid research are discussed. The paper focuses on various practical and theoretical considerations involved in processing architecture, gait generation and vision systems.
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paper_title: Postural Stability of Biped Robots and the Foot-Rotation Indicator (FRI) Point

paper_content:

The focus of this paper is the problem of foot rotation in biped robots during the single-support phase. Foot rotation is an indication of postural instability, which should be carefully treated in a dynamically stable walk and avoided altogether in a statically stable walk. We introduce the foot-rotation indicator (FRI) point, which is a point on the foot/ground-contact surface where the net ground-reaction force would have to act to keep the foot stationary. To ensure no foot rotation, the FRI point must remain within the convex hull of the foot-support area. In contrast with the ground projection of the center of mass (GCoM), which is a static criterion, the FRI point incorporates robot dynamics. As opposed to the center of pressure (CoP)—better known as the zero-moment point (ZMP) in the robotics literature—which may not leave the support area, the FRI point may leave the area. In fact, the position of the FRI point outside the footprint indicates the direction of the impending rotation and the magnitude of rotational moment acting on the foot. Owing to these important properties, the FRI point helps not only to monitor the state of postural stability of a biped robot during the entire gait cycle, but indicates the severity of instability of the gait as well. In response to a recent need, the paper also resolves the misconceptions surrounding the CoP/ZMP equivalence.
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paper_title: Control of walking robots based on manipulation of the zero moment point

paper_content:

In this paper, a new application of the ZMP (Zero Moment Point) control law is presented. The objective of this control method is to obtain a smooth and soft motion based on a real-time control. In the controller, the ZMP is treated as an actuating signal. The coordinates of the robot body are fed back to obtain its position. The proposed control method was applied on two different biped robots, and its validity is verified experimentally.
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paper_title: Compliant Terrain Adaptation for Biped Humanoids Without Measuring Ground Surface and Contact Forces

paper_content:

This paper reports the applicability of our passivity-based contact force control framework for biped humanoids. We experimentally demonstrate its adaptation to unknown rough terrain. Adaptation to uneven ground is achieved by optimally distributed antigravitational forces applied to preset contact points in a feedforward manner, even without explicitly measuring the external forces or the terrain shape. Adaptation to unknown inclination is also possible by combining an active balancing controller based on the center-of-mass (CoM) measurements with respect to the inertial frame. Furthermore, we show that a simple impedance controller for supporting the feet or hands allows the robot to adapt to low-friction ground without prior knowledge of the ground friction. This presentation includes supplementary experimental videos that show a full-sized biped humanoid robot balancing on uneven ground or time-varying inclination.
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paper_title: ZERO-MOMENT POINT — THIRTY FIVE YEARS OF ITS LIFE

paper_content:

In Vol. 1, No. 1 of Int. J. Humanoid Rubotics, we gave a review of the Zero-Moment Point (ZMP) concept, on the occasion of thirty five years of its use in modeling dynamics of the biped gait. At the time of writing this article, we thought that thirty five years was a long enough period in which all confusions, if they existed, could be cleared up. However, we were not quite right, and we will indicate here some of the most important points concerning the concept. A survey of the literature that appeared in the last several years related to the widely accepted ZMP method reveals a diversity of terms used to describe the state in which the biped system performs “regular” gait, safe from overturning. The terms in use are: stability (stable gait), dynamic balance, and dynamic equilibrium. Stability is a customary term in the theory of automatic control, and in this domain it has its well-defined meaning and usage, so that it is not appropriate to use it in some other instances, straying from its basic meaning. The term dynamic equilibrium is also unsuitable because it is used in the D’Alambert principle to transform dynamic equations into a static form, with a zero on the right-hand side — hence the term equilibrium. Therefore, dynamic balance remains the most appropriate term, as it fully reflects the nature of the related notion, and is not in use in other areas. We ourselves did not make clear this terminological distinction in the aforementioned article, which would have been prudent. Another issue we want to address is the situation illustrated in Fig. 4 in the paper — to make it easier to follow the explanation we repeat it here — concerning the notions of ZMP and fictitious ZMP (FZMP). As is well known, the crucial characteristic of a dynamically balanced gait is that the point at which the conditions ΣMx = 0, ΣMy = 0 are fulfilled is within the support polygon (Fig. 4), whereby
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paper_title: Ground Reference Points in Legged Locomotion: Definitions, Biological Trajectories and Control Implications

paper_content:

The zero moment point (ZMP), foot rotation indicator (FRI) and centroidal moment pivot (CMP) are important ground reference points used for motion identification and control in biomechanics and legged robotics. In this paper, we study these reference points for normal human walking, and discuss their applicability in legged machine control. Since the FRI was proposed as an indicator of foot rotation, we hypothesize that the FRI will closely track the ZMP in early single support when the foot remains flat on the ground, but will then significantly diverge from the ZMP in late single support as the foot rolls during heel-off. Additionally, since spin angular momentum has been shown to remain small throughout the walking cycle, we hypothesize that the CMP will never leave the ground support base throughout the entire gait cycle, closely tracking the ZMP. We test these hypotheses using a morphologically realistic human model and kinetic and kinematic gait data measured from ten human subjects walking at self-...
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paper_title: Running in Three Dimensions: Analysis of a Point-mass Sprung-leg Model

paper_content:

We analyze a simple model for running: a three-dimensional spring-loaded inverted pendulum carrying a point mass (3D-SLIP). Our formulation reduces to the sagittal plane SLIP and horizontal plane lateral leg spring (LLS) models in the appropriate limits. Using the intrinsic geometry and symmetries and appealing to the case of stiff springs, in which gravity may be neglected during stance, we derive an explicit approximate mapping describing stride-to-stride behavior. We thereby show that all left-right symmetric periodic gaits are unstable, deriving a particularly simple mapping for sagittal plane dynamics. Continuation to fixed points for the "exact" mapping confirms instability of these gaits, and we describe a simple feedback stabilization scheme for leg placement at touchdown.
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paper_title: On the mechanics of natural compliance in frictional contacts and its effect on grasp stiffness and stability

paper_content:

This paper considers the effect of natural material compliance on the stiffness and stability of frictional multi-contact grasps and fixtures. The contact preload profile is a key parameter in the nonlinear compliance laws governing such contacts. The paper introduces the Hertz-Walton contact compliance model which is valid for linear contact loading profiles. The model is specified in a lumped parameter form suitable for on-line grasping applications, and is entirely determined by the contact friction and by the material and geometric properties of the contacting bodies. The model predicts an asymmetric stiffening of the tangential reaction force as the normal load at the contact increases. As a result, the composite stiffness matrix of multi-contact grasps governed by natural compliance effects is asymmetric, indicating that these contact arrangements are not governed by any potential energy function. Based on the compliant grasp dynamics, the paper derives rules indicating which contact point locations and what preload profiles guarantee grasp and fixture stability. The paper also describes preliminary experiments supporting the contact model predictions.
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paper_title: Biped robot design powered by antagonistic pneumatic actuators for multi-modal locomotion

paper_content:

An antagonistic muscle mechanism that regulates joint compliance contributes enormously to human dynamic locomotion. Antagonism is considered to be the key for realizing more than one locomotion mode. In this paper, we demonstrate how antagonistic pneumatic actuators can be utilized to achieve three dynamic locomotion modes (walking, jumping, and running) in a biped robot. Firstly, we discuss the contribution of joint compliance to dynamic locomotion, which highlights the importance of tunable compliance. Secondly, we introduce the design of a biped robot powered by antagonistic pneumatic actuators. Lastly, we apply simple feedforward controllers for realizing walking, jumping, and running and confirm the contribution of joint compliance to such multimodal dynamic locomotion. Based on the results, we can conclude that the antagonistic pneumatic actuators are superior candidates for constructing a human-like dynamic locomotor.
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paper_title: Reactive reflex-based control for a four-legged walking machine

paper_content:

Abstract This paper presents methods and experiments of a reactive control architecture for a four-legged walking machine. Starting with a description of the existing control architecture we introduce the concepts of reflexes and behaviours as well as their integration into the system. The used reactive network and the development process is described in detail. The paper concludes with a description of various experiments.
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paper_title: Modeling and Experiments of Untethered Quadrupedal Running with a Bounding Gait: The Scout II Robot

paper_content:

In this paper we compare models and experiments involving Scout II, an untethered four-legged running robot with only one actuator per compliant leg. Scout II achieves dynamically stable running of up to 1.3 m s-1 on flat ground via a bounding gait. Energetics analysis reveals a highly efficient system with a specific resistance of only 1.4. The running controller requires no task-level or body-state feedback, and relies on the passive dynamics of the mechanical system. These results contribute to the increasing evidence that apparently complex dynamically dexterous tasks may be controlled via simple control laws. We discuss general modeling issues for dynamically stable legged robots. Two simulation models are compared with experimental data to test the validity of common simplifying assumptions. The need for including motor saturation and non-rigid torque transmission characteristics in simulation models is demonstrated. Similar issues are likely to be important in other dynamically stable legged robots as well. An extensive suite of experimental results documents the robot's performance and the validity of the proposed models.
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paper_title: Stability Analysis of Legged Locomotion Models by Symmetry-Factored Return Maps

paper_content:

We present a new stability analysis for hybrid legged locomotion systems based on the “symmetric” factorization of return maps. We apply this analysis to two-degrees-of-freedom (2DoF) and three-degrees-of-freedom (3DoF) models of the spring loaded inverted pendulum (SLIP) with different leg recirculation strategies. Despite the non-integrability of the SLIP dynamics, we obtain a necessary condition for asymptotic stability (and a sufficient condition for instability) at a fixed point, formulated as an exact algebraic expression in the physical parameters. We use this expression to characterize analytically the sensory cost and stabilizing benefit of various feedback schemes previously proposed for the 2DoF SLIP model, posited as a low-dimensional representation of running. We apply the result as well to a 3DoF SLIP model that will be treated at greater length in a companion paper as a descriptive model for the robot RHex.
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paper_title: On the Improvement of Walking Performance in Natural Environments by a Compliant Adaptive Gait

paper_content:

It is a widespread idea that animal-legged locomotion is better than wheeled locomotion on natural rough terrain. However, the use of legs as a locomotion system for vehicles and robots still has a long way to go before it can compete with wheels and trucks, even on natural ground. This paper aims to solve two main disadvantages plaguing walking robots: their inability to react to external disturbances (which is also a drawback of wheeled robots); and their extreme slowness. Both problems are reduced here by combining: 1) a gait-parameter-adaptation method that maximizes a dynamic energy stability margin and 2) an active-compliance controller with a new term that compensates for stability variations, thus helping the robot react stably in the face of disturbances. As a result, the combined gait-adaptation approach helps the robot achieve faster, more stable compliant motions than conventional controllers. Experiments performed with the SILO4 quadruped robot show a relevant improvement in the walking gait
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paper_title: A Trotting Horse Model

paper_content:

A new control strategy is used to stabilize numerical simulations of a horse model in the trotting quadrupedal gait. Several well-established experimental findings are predicted by the model, including how stride frequency and stride length change with forward running speed. Mass is distributed throughout the model's legs, trunk, and head in a realistic manner. Leg and trunk flexion is modeled using four flexible legs, a back joint, and a neck joint. In the control model, pitch stabilization is achieved without directly controlling body pitch, but rather by controlling both the aerial time and the foot speed of each stance leg. The legs behave as ideal springs while in contact with the ground, enabling the model to rebound from the ground with each trotting step. Numerical experiments are conducted to test the model's capacity to overcome a change in ground impedance. Model stability is maximized and the metabolic cost of trotting is minimized within a narrow range of leg stiffness where trotting horses o...
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paper_title: A Control Approach for Actuated Dynamic Walking in Biped Robots

paper_content:

This paper presents an approach for the closed-loop control of a fully actuated biped robot that leverages its natural dynamics when walking. Rather than prescribing kinematic trajectories, the approach proposes a set of state-dependent torques, each of which can be constructed from a combination of low-gain spring-damper couples. Accordingly, the limb motion is determined by interaction of the passive control elements and the natural dynamics of the biped, rather than being dictated by a reference trajectory. In order to implement the proposed approach, the authors develop a model-based transformation from the control torques that are defined in a mixed reference frame to the actuator joint torques. The proposed approach is implemented in simulation on an anthropomorphic biped. The simulated biped is shown to converge to a stable, natural-looking walk from a variety of initial configurations. Based on these simulations, the mechanical cost of transport is computed and shown to be significantly lower than that of trajectory-tracking approaches to biped control, thus validating the ability of the proposed idea to provide efficient dynamic walking. Simulations further demonstrate walking at varying speeds and on varying ground slopes. Finally, controller robustness is demonstrated with respect to forward and backward push-type disturbances and with respect to uncertainty in model parameters.
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paper_title: An effective trajectory generation method for bipedal walking

paper_content:

This paper presents the virtual height inverted pendulum mode (VHIPM), which is a simple and effective trajectory generation method for the stable walking of biped robots. VHIPM, which is based on the inverted pendulum mode (IPM), can significantly reduce the zero moment point (ZMP) error by adjusting the height in the inverted pendulum. We show the relationship between VHIPM and other popular trajectory generation methods, and compare the ZMP errors in walking when trajectories are generated by various methods including VHIPM. We also investigate the sensitivity of the ZMP error in VHIPM to the step length, walking period and mass distribution of a robot. The simulation results show that VHIPM significantly reduces the ZMP errors compared to other methods under various circumstances.
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paper_title: Application of Genetic Algorithms for biped robot gait synthesis optimization during walking and going up-stairs

paper_content:

Selecting an appropriate gait can reduce consumed energy by a biped robot. In this paper, a Genetic Algorithm gait synthesis method is proposed, which generates the angle trajectories based on the minimum consumed energy and minimum torque change. The gait synthesis is considered for two cases: walking and going up-stairs. The proposed method can be applied for a wide range of step lengths and step times during walking; or step lengths, stair heights and step times for going up-stairs. The angle trajectories are generated without neglecting the stability of the biped robot. The angle trajectories can be generated for other tasks to be performed by the biped robot, like going down-stairs, overcoming obstacles, etc. In order to verify the effectiveness of the proposed method, the results for minimum consumed energy and minimum torque change are compared. A Radial Basis Function Neural Network is considered for the real-time application. Simulations are realized based upon the parameters of the 'Bonten-Maru ...
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paper_title: A combined potential function and graph search approach for free gait generation of quadruped robots

paper_content:

This paper presents an algorithm for planning the foothold positions of quadruped robots on irregular terrain. The input to the algorithm is the robot kinematics, the terrain geometry, a required motion path, as well as initial posture. Our goal is to develop general algorithm that navigate quadruped robots quasi-statically over rough terrain, using an APF (Artificial Potential Field) and graph searching. The algorithm is planning a sequence set of footholds that navigates the robot along the required path with controllable motion characteristics. Simulations results demonstrate the algorithm in a planner environment.
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paper_title: Experimental Validation of a Framework for the Design of Controllers that Induce Stable Walking in Planar Bipeds

paper_content:

In this paper we present the experimental validation of a framework for the systematic design, analysis, and performance enhancement of controllers that induce stable walking in N -link underactuated planar biped robots. Controllers designed via this framework act by enforcing virtual constraints—holonomic constraints imposed via feedback—on the robot’s configuration, which create an attracting two-dimensional invariant set in the full walking model’s state space. Stability properties of resulting walking motions are easily analyzed in terms of a two-dimensional subdynamic of the full walking model. A practical introduction to and interpretation of the framework is given. In addition, in this paper we develop the ability to regulate the average walking rate of the biped to a continuum of values by modification of within-stride and stride-boundary characteristics, such as step length.
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paper_title: Efficient Walking Speed Optimization of a Humanoid Robot

paper_content:

The development of optimized motions of humanoid robots that guarantee fast and also stable walking is an important task, especially in the context of autonomous soccer-playing robots in RoboCup. We present a walking motion optimization approach for the humanoid robot prototype HR18 which is equipped with a low-dimensional parameterized walking trajectory generator, joint motor controller and an internal stabilization. The robot is included as hardware-in-the-loop to define a low-dimensional black-box optimization problem. In contrast to previously performed walking optimization approaches, we apply a sequential surrogate optimization approach using stochastic approximation of the underlying objective function and sequential quadratic programming to search for a fast and stable walking motion. This is done under the conditions that only a small number of physical walking experiments should have to be carried out during the online optimization process. For the identified walking motion for the considered 55 cm tall humanoid robot, we measured a forward walking speed of more than 30 cm s-1 . With a modified version of the robot, even more than 40 cm s-1 could be achieved in permanent operation.
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paper_title: Generation of free gait-a graph search approach

paper_content:

A method is presented for the generation of a free gait for the straight-line motion of a quadruped walking machine. It uses a heuristic graph search procedure based on the A* algorithm. The method essentially looks into the consequences of a move to a certain depth before actually committing to it. Deadlocks and inefficiencies are thus sensed well in advance and avoided. >
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paper_title: A Parametric Optimization Approach to Walking Pattern Synthesis

paper_content:

Walking pattern synthesis is carried out using a spline-based parametric optimization technique. Generalized coordinates are approximated by spline functions of class C3fitted at knots uniformly distributed along the motion time. This high-order differentiability eliminates jerky variations of actuating torques. Through connecting conditions, spline polynomial coefficients are determined as a linear function of the joint coordinates at knots. These values are then dealt with as optimization parameters. An optimal control problem is formulated on the basis of a performance criterion to be minimized, representing an integral quadratic amount of driving torques. Using the above spline approximations, this primary problem is recast into a constrained non-linear optimization problem of mathematical programming, which is solved using a computing code implementing an SQP algorithm. As numerical simulations, complete gait cycles are generated for a seven-link planar biped. The only kinematic data to be accounted for are the walking speeds. Optimization of both phases of gait is carried out globally; it includes the optimization of transition configurations of the biped between successive phases of the gait cycle.
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paper_title: Real time gait generation for autonomous humanoid robots: A case study for walking

paper_content:

Abstract As autonomous humanoid robots assume more important roles in everyday life, they are expected to perform many different tasks and quickly adapt to unknown environments. Therefore, humanoid robots must generate quickly the appropriate gait based on information received from visual system. In this work, we present a new method for real time gait generation during walking based on Neural Networks. The minimum consumed energy gaits similar with human motion, are used to teach the Neural Network. After supervised learning, the Neural Network can quickly generate the humanoid robot gait. Simulation and experimental results utilizing the “Bonten-Maru I” humanoid robot show good performance of the proposed method.
---
paper_title: A Simplified Stability Study for a Biped Walk with Underactuated and Overactuated Phases

paper_content:

This paper is devoted to a stability study of a walking gait for a biped. The walking gait is periodic and it is composed of a single-support phase, a passive impact, and a double-support phase. The reference trajectories are described as a function of the shin orientation versus the ground of the stance leg. We use the Poincare map to study the stability of the walking gait of the biped. We only study the stability of dynamics not controlled during the single-support phase, i.e., the dynamics of the shin angle. We then suppose there is no perturbation in the tracking of the references of the other joint angles of the biped. The studied Poincare map is then of dimension one. With a particular control law in double support, it is shown theoretically and in simulation that a perturbation error in the velocity of the shin angle can be eliminated in one step only. The zone of convergence in one step is determined. The condition of existence of a cyclic gait is given, and for a given cyclic gait, the stability condition is also given. It is shown that due to the given control law for the overactuated double-support phase, a cyclic motion is practically guaranteed to be stable. It should be noted it is possible for the biped to reach a periodic regime from a stopped position in one step.
---
paper_title: Survey of locomotion control of legged robots inspired by biological concept

paper_content:

Compared with wheeled mobile robots, legged robots can easily step over obstacles and walk through rugged ground. They have more flexible bodies and therefore, can deal with complex environment. Nevertheless, some other issues make the locomotion control of legged robots a much complicated task, such as the redundant degree of freedoms and balance keeping. From literatures, locomotion control has been solved mainly based on programming mechanism. To use this method, walking trajectories for each leg and the gaits have to be designed, and the adaptability to an unknown environment cannot be guaranteed. From another aspect, studying and simulating animals’ walking mechanism for engineering application is an efficient way to break the bottleneck of locomotion control for legged robots. This has attracted more and more attentions. Inspired by central pattern generator (CPG), a control method has been proved to be a successful attempt within this scope. In this paper, we will review the biological mechanism, the existence evidences, and the network properties of CPG. From the engineering perspective, we will introduce the engineering simulation of CPG, the property analysis, and the research progress of CPG inspired control method in locomotion control of legged robots. Then, in our research, we will further discuss on existing problems, hot issues, and future research directions in this field.
---
paper_title: In vitro reconstruction of the respiratory central pattern generator of the mollusk Lymnaea.

paper_content:

Most rhythmic behaviors such as respiration, locomotion, and feeding are under the control of networks of neurons in the central nervous system known as central pattern generators (CPGs). The respiratory rhythm of the pond snail Lymnaea stagnalis is a relatively simple, CPG-based behavior for which the underlying neural elements have been identified. A three-neuron network capable of generating the respiratory rhythm of this air-breathing mollusk has been reconstructed in culture. The intrinsic and network properties of this neural ensemble have been studied, and the mechanism of postinhibitory rebound excitation was found to be important for the rhythm generation. This in vitro model system enables a better understanding of the neural basis of rhythm generation.
---
paper_title: On Central Pattern Generator of Biological Motor System

paper_content:

This paper presents theoretical results of the neural control mechanism existed in the spinal cord, central pattern generator (CPG), which has the ability to provide rhythmic movement patterns for the invertebrate and vertebrate. It is known that although CPG is verified by biological methods, it still lacks a complete theoretical investigation. The theoretical analysis about CPG from engineering perspective such as parameter selection and conditions of stable oscillation will strengthen the foundation of CPG theory, and it will significantly enhance the effective application of CPG into motor control systems.
---
paper_title: Central pattern generators for locomotion control in animals and robots: a review

paper_content:

The problem of controlling locomotion is an area in which neuroscience and robotics can fruitfully interact. In this article, I will review research carried out on locomotor central pattern generators (CPGs), i.e. neural circuits capable of producing coordinated patterns of high-dimensional rhythmic output signals while receiving only simple, low-dimensional, input signals. The review will first cover neurobiological observations concerning locomotor CPGs and their numerical modelling, with a special focus on vertebrates. It will then cover how CPG models implemented as neural networks or systems of coupled oscillators can be used in robotics for controlling the locomotion of articulated robots. The review also presents how robots can be used as scientific tools to obtain a better understanding of the functioning of biological CPGs. Finally, various methods for designing CPGs to control specific modes of locomotion will be briefly reviewed. In this process, I will discuss different types of CPG models, the pros and cons of using CPGs with robots, and the pros and cons of using robots as scientific tools. Open research topics both in biology and in robotics will also be discussed.
---
paper_title: Central pattern generators and the control of rhythmic movements

paper_content:

Central pattern generators are neuronal circuits that when activated can produce rhythmic motor patterns such as walking, breathing, flying, and swimming in the absence of sensory or descending inputs that carry specific timing information. General principles of the organization of these circuits and their control by higher brain centers have come from the study of smaller circuits found in invertebrates. Recent work on vertebrates highlights the importance of neuromodulatory control pathways in enabling spinal cord and brain stem circuits to generate meaningful motor patterns. Because rhythmic motor patterns are easily quantified and studied, central pattern generators will provide important testing grounds for understanding the effects of numerous genetic mutations on behavior. Moreover, further understanding of the modulation of spinal cord circuitry used in rhythmic behaviors should facilitate the development of new treatments to enhance recovery after spinal cord damage.
---
paper_title: Biomimetic Walking Robot Scorpion: Control and Modeling

paper_content:

Abstract We present the biomimetic control scheme for the walking robot SCORPION. We used a concept of Basic Motion Patterns, which can be combined in a very flexible manner. Also reflexes are introduced to increase the reactivity. In addition our modeling and simulation approach is described, which has been done based on the ADAMS™ simulator. Especially the motion patterns of real scorpions were analyzed and used for walking patterns and smooth acceleration of the robot.
---
paper_title: BoxyBot: a swimming and crawling fish robot controlled by a central pattern generator

paper_content:

We present a novel fish robot capable of swimming and crawling. The robot is driven by DC motors and has three actuated fins, with two pectoral fins and one caudal fin. It is loosely inspired from the boxfish. The control architecture of the robot is constructed around a central pattern generator (CPG) implemented as a system of coupled nonlinear oscillators, which, like its biological counterpart, can produce coordinated patterns of rhythmic activity while being modulated by simple control parameters. Using the CPG model, the robot is capable of performing and switching between a variety of different locomotor behaviors such as swimming forwards, swimming backwards, turning, rolling, moving upwards/downwards, and crawling. These behaviors are triggered and modulated by sensory input provided by light and water sensors. Results are presented demonstrating the agility of the robot, and interesting properties of a CPG-based control approach such as stability of the rhythmic patterns due to limit cycle behavior, and the production of smooth trajectories despite abrupt changes of control parameters
---
paper_title: Experimentally Verified Optimal Serpentine Gait and Hyperredundancy of a Rigid-Link Snake Robot

paper_content:

In this study, we examine, for a six-link snake robot, how an optimal gait might change as a function of the snake- surface interaction model and how the overall locomotion performance changes under nonoptimal conditions such as joint failure. Simulations are evaluated for three different types of friction models, and it is shown that the gait parameters for serpentine motion are very dependant on the frictional model if minimum power expenditure is desired for a given velocity. Experimental investigations then motivate a surface interaction model not commonly used in snake locomotion studies. Using this new model, simulation results are compared to experiments for nominal and nonnominal locomotion cases including actuator faults. It is shown that this model quite accurately predicts locomotion velocities and link profiles, but that the accuracy of these predictions degrades severely at speeds where actuator dynamics become significant.
---
paper_title: Experimental Verification of Open-loop Control for an Underwater Eel-like Robot

paper_content:

In this paper, we describe experimental work using an underwater, biomimetic, eel-like robot to verify a simplified dynamic model and open-loop control routines. We compare experimental results to previous analytically derived, but approximate expressions for proposed gaits for forward/backward swimming, circular swimming, sideways swimming and turning in place. We have developed a five-link, underwater eel-like robot, focusing on modularity, reliability and rapid prototyping, to verify our theoretical predictions. Results from open-loop experiments performed with this robot in an aquatic environment using an off-line vision system for position sensing show good agreement with theory.
---
paper_title: Soccer playing humanoid robots: Processing architecture, gait generation and vision system

paper_content:

Research on humanoid robotics in Mechatronics and Automation (MA) Laboratory, Electrical and Computer Engineering (ECE), National University of Singapore (NUS) was started at the beginning of this decade. Various research prototypes for humanoid robots have been designed and are going through evolution over these years. These humanoids have been successfully participating in various robotic soccer competitions. In this paper, three major research and development aspects of the above humanoid research are discussed. The paper focuses on various practical and theoretical considerations involved in processing architecture, gait generation and vision systems.
---
paper_title: A Biologically Inspired Biped Locomotion Strategy for Humanoid Robots: Modulation of Sinusoidal Patterns by a Coupled Oscillator Model

paper_content:

Biological systems seem to have a simpler but more robust locomotion strategy than that of the existing biped walking controllers for humanoid robots. We show that a humanoid robot can step and walk using simple sinusoidal desired joint trajectories with their phase adjusted by a coupled oscillator model. We use the center-of-pressure location and velocity to detect the phase of the lateral robot dynamics. This phase information is used to modulate the desired joint trajectories. We do not explicitly use dynamical parameters of the humanoid robot. We hypothesize that a similar mechanism may exist in biological systems. We applied the proposed biologically inspired control strategy to our newly developed human-sized humanoid robot computational brain (CB) and a small size humanoid robot, enabling them to generate successful stepping and walking patterns.
---
paper_title: Construction of Central Pattern Generator Using Piecewise Affine Systems

paper_content:

Central pattern generator (CPG) is a neural circuit which governs rhythmic activities of animal. Synthesis of CPG plays an important role in engineering such as locomotion control. Up to now, CPGs are mainly derived based on biological principles, with their parameters heuristically tuned. In this paper, a class of piecewise affine systems capable of exhibiting CPG-like property of stable limit cycles is presented. A proposed synthesis method for CPGs is also included.
---
paper_title: Design of a novel central pattern generator and the hebbian motion learning

paper_content:

In this paper, we propose a new CPG model and hebbian learning rule for the CPG. The output of the proposed CPG is determined by only phase differences of synchronization of component oscillators. A phase synchronization can be regarded as an adaptive behavior to an environment, so the CPG has an adaptavility despite of only simple connections to the environment. We also propose a motion learning rule for the proposed CPG. Since the rule is described by only simple signal processing, so that it can be realized by electric circuits easily, it will be able to be used efficiently for high degree of freedom robots.
---
paper_title: An analog CMOS central pattern generator for interlimb coordination in quadruped locomotion

paper_content:

This paper proposes a neuromorphic analog CMOS controller for interlimb coordination in quadruped locomotion. Animal locomotion, such as walking, running, swimming, and flying, is based on periodic rhythmic movements. These rhythmic movements are driven by the biological neural network, called the central pattern generator (CPG). In recent years, many researchers have applied CPG to locomotion controllers in robotics. However, most of these have been developed with digital processors and, thus, have several problems, such as high power consumption. In order to overcome such problems, a CPG controller with analog CMOS circuit is proposed. Since the CMOS transistors in the circuit operate in their subthreshold region and under low supply voltage, the controller can reduce power consumption. Moreover, low-cost production and miniaturization of controllers are expected. We have shown through computer simulation, such circuit has the capability to generate several periodic rhythmic patterns and transitions between their patterns promptly.
---
paper_title: Configuring of Spiking Central Pattern Generator Networks for Bipedal Walking Using Genetic Algorthms

paper_content:

In limbed animals, spinal neural circuits responsible for controlling muscular activities during walking are called central pattern generators (CPG). CPG networks display oscillatory activities that actuates individual or groups of muscles in a coordinated fashion so that the limbs of the animal are flexed and extended at the appropriate time and with the required velocity for the animal to efficiently traverse various types of terrain, and to recover from environmental perturbation. Typically, the CPG networks are constructed with many neurons, each of which has a number of control parameters. As the number of muscles increases, it is often impossible to manually, albeit intelligently, select the network parameters for a particular movement. Furthermore, it is virtually impossible to reconfigure the parameters on-line. This paper describes how genetic algorithms (GA) can be used for on-line (re)configuring of CPG networks for a bipedal robot. We show that the neuron parameters and connection weights/network topology of a canonical walking network can be reconfigured within a few of generations of the GA. The networks, constructed with integrate-and-fire-with-adaptation (IFA) neurons, are implemented with a microcontroller and can be reconfigured to vary walking speed from 0.5Hz to 3.5Hz. The phase relationship between the hips and knees can be arbitrarily set (to within 1 degree) and prescribed complex joint angle profiles are realized. This is a powerful approach to generating complex muscle synergies for robots with multiple joints and distributed actuators.
---
paper_title: A central pattern generator for insect gait production

paper_content:

We present a neural network model inspired from both behavioral and neurophysiological data on insect locomotion. The model consists of a central rhythmic pattern generator and a sensory motor network. We show that it exhibits various behavioral properties observed in several insect species: it produces a continuum of stable gaits ranging from metachronal to tripod; the changes in the duration of protraction retraction phases for various walking speeds follow closely the behavorial data; the network is insensitive to the initial position of the legs, in that it can achieve rapidly a coherent phase relationship regardless of the initial conditions. In addition, this transient property also extends to situations where rapid changes occur in the walking speed; the network can reorganize its phase rapidly during fast accelerations as well as fast decelerations.
---
paper_title: Neural control of interlimb oscillations

paper_content:

How do humans and other animals accomplish coordinated movements? How are novel combinations of limb joints rapidly assembled into new behavioral units that move together in in-phase or anti-phase movement patterns during complex movement tasks? A neural central pattern generator (CPG) model simulates data from human bimanual coordination tasks. As in the data, anti-phase oscillations at low frequencies switch to in-phase oscillations at high frequencies, in-phase oscillations occur at both low and high frequencies, phase fluctuations occur at the anti-phase in-phase transition, a “seagull effect” of larger errors occurs at intermediate phases, and oscillations slip toward in-phase and anti-phase when driven at intermediate phases. These oscillations and bifurcations are emergent properties of the CPG model in response to volitional inputs. The CPG model is a version of the Ellias-Grossberg oscillator. Its neurons obey Hodgkin-Huxley type equations whose excitatory signals operate on a faster time scale than their inhibitory signals in a recurrent on-center off-surround anatomy. When an equal command or GO signal activates both model channels, the model CPG can generate both in-phase and anti-phase oscillations at different GO amplitudes. Phase transitions from either in-phase to anti-phase oscillations, or from anti-phase to in-phase oscillations, can occur in different parameter ranges, as the GO signal increases.
---
paper_title: Programmable central pattern generators: an application to biped locomotion control

paper_content:

We present a system of coupled nonlinear oscillators to be used as programmable central pattern generators, and apply it to control the locomotion of a humanoid robot. Central pattern generators are biological neural networks that can produce coordinated multidimensional rhythmic signals, under the control of simple input signals. They are found both in vertebrate and invertebrate animals for the control of locomotion. In this article, we present a novel system composed of coupled adaptive nonlinear oscillators that can learn arbitrary rhythmic signals in a supervised learning framework. Using adaptive rules implemented as differential equations, parameters such as intrinsic frequencies, amplitudes, and coupling weights are automatically adjusted to replicate a teaching signal. Once the teaching signal is removed, the trajectories remain embedded as the limit cycle of the dynamical system. An interesting aspect of this approach is that the learning is completely embedded into the dynamical system, and does not require external optimization algorithms. We use our system to encapsulate rhythmic trajectories for biped locomotion with a simulated humanoid robot, and demonstrate how it can be used to do online trajectory generation. The system can modulate the speed of locomotion, and even allow the reversal of direction (i.e. walking backwards). The integration of sensory feedback allows the online modulation of trajectories such as to increase the basin of stability of the gaits, and therefore the range of speeds that can be produced
---
paper_title: Adaptive Dynamic Walking of a Quadruped Robot on Irregular Terrain Based on Biological Concepts

paper_content:

We have been trying to induce a quadruped robot to walk with medium walking speed on irregular terrain based on biological concepts. We propose the necessary conditions for stable dynamic walking on irregular terrain in general, and we design the mechanical system and the neural system by comparing biological concepts with those necessary conditions described in physical terms. A PD controller at the joints can construct the virtual spring-damper system as the visco-elasticity model of a muscle. The neural system model consists of a central pattern generator (CPG) and reflexes. A CPG receives sensory input and changes the period of its own active phase. The desired angle and P-gain of each joint in the virtual spring-damper system is switched based on the phase signal of the CPG. CPGs, the motion of the virtual spring-damper system of each leg and the rolling motion of the body are mutually entrained through the rolling motion feedback to CPGs, and can generate adaptive walking. We report on our experimen...
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paper_title: Passive compliant quadruped robot using Central Pattern Generators for locomotion control

paper_content:

We present a new quadruped robot, ldquoCheetahrdquo, featuring three-segment pantographic legs with passive compliant knee joints. Each leg has two degrees of freedom - knee and hip joint can be actuated using proximal mounted RC servo motors, force transmission to the knee is achieved by means of a bowden cable mechanism. Simple electronics to command the actuators from a desktop computer have been designed in order to test the robot. A Central Pattern Generator (CPG) network has been implemented to generate different gaits. A parameter space search was performed and tested on the robot to optimize forward velocity.
---
paper_title: Research on gait planning of artificial leg based on central pattern generator

paper_content:

Biped robot with heterogeneous legs (BRHL) is a novel robot model, which consists of an artificial leg and an intelligent bionic leg. The artificial leg is used to simulate the amputeepsilas healthy leg and the bionic leg works as the intelligent artificial limb. To describe the present gait of the healthy leg and make intelligent bionic leg follow the walking of artificial leg in all phases is the target of BRHLpsilas research. So gait planning of artificial leg is the emphasis of BRHLpsilas research. This paper uses the model of central pattern generator (CPG) in the research of artificial legpsilas gait planning from the point of biology. To obtain natural and robust walking pattern, genetic algorithm is used to optimize parameters of the CPG network model and the fitness function is formulated based on zero moment point (ZMP). Simulation results testify the feasibility of this method.
---
paper_title: Slip-adaptive walk of quadruped robot

paper_content:

Abstract In this paper, we investigated the effects of the friction condition on walking pattern and energy efficiency, and based on the results, we proposed two new “slip-adaptive” strategies for generating a slip-adaptive walk. The first strategy for a slip-adaptive walk uses a slip reflex via a Central Pattern Generator (CPG) to change the walking pattern. The second strategy for a walk uses a force control to immediately compensate a slip. Using these strategies, a walk, which is adaptive to varying friction conditions and slips, becomes possible. The validity of the proposed method is confirmed through simulation and experimentation.
---
paper_title: Sustained oscillations generated by mutually inhibiting neurons with adaptation

paper_content:

Autonomic oscillatory activities exist in almost every living thing and most of them are produced by rhythmic activities of the corresponding neural systems (locomotion, respiration, heart beat, etc.). This paper mathematically discusses sustained oscillations generated by mutual inhibition of the neurons which are represented by a continuous-variable model with a kind of fatigue or adaptation effect. If the neural network has no stable stationary state for constant input stimuli, it will generate and sustain some oscillation for any initial state and for any disturbance. Some sufficient conditions for that are given to three types of neural networks: lateral inhibition networks of linearly arrayed neurons, symmetric inhibition networks and cyclic inhibition networks. The result suggests that the adaptation of the neurons plays a very important role for the appearance of the oscillations. Some computer simulations of rhythmic activities are also presented for cyclic inhibition networks consisting of a few neurons.
---
paper_title: Learning CPG-based Biped Locomotion with a Policy Gradient Method: Application to a Humanoid Robot

paper_content:

In this paper we describe a learning framework for a central pattern generator (CPG)-based biped locomotion controller using a policy gradient method. Our goals in this study are to achieve CPG-based biped walking with a 3D hardware humanoid and to develop an efficient learning algorithm with CPG by reducing the dimensionality of the state space used for learning. We demonstrate that an appropriate feedback controller can be acquired within a few thousand trials by numerical simulations and the controller obtained in numerical simulation achieves stable walking with a physical robot in the real world. Numerical simulations and hardware experiments evaluate the walking velocity and stability. The results suggest that the learning algorithm is capable of adapting to environmental changes. Furthermore, we present an online learning scheme with an initial policy for a hardware robot to improve the controller within 200 iterations.
---
paper_title: Neural networks that co-ordinate locomotion and body orientation in lamprey

paper_content:

The networks of the brainstem and spinal cord that co-ordinate locomotion and body orientation in lamprey are described. The cycle-to-cycle pattern generation of these networks is produced by interacting glutamatergic and glycinergic neurones, with NMDA receptor-channels playing an important role at lower rates of locomotion. The fine tuning of the networks produced by 5-HT, dopamine and GABA systems involves a modulation of Ca2+-dependent K+ channels, high- and low-threshold voltage-activated Ca2+ channels and presynaptic inhibitory mechanisms. Mathematical modelling has been used to explore the capacity of these biological networks. The vestibular control of the body orientation during swimming is exerted via reticulospinal neurones located in different reticular nuclei. These neurones become activated maximally at different angles of tilt.
---
paper_title: Bipedal locomotion control using a four-compartmental central pattern generator

paper_content:

In this paper, we develop a simple bipedal locomotion algorithm based on biological concepts. The algorithm utilized a central pattern generator (CPG) composed of four coupled neural oscillators (NO) to generate control signal for the bipedal robot. Feedbacks from the robot dynamics and the environment are used to update the CPG online. Our algorithm is then tested on a seven link model of bipedal robot. Simulation results suggest the proposed CPG can generate a smooth and continuous walking pattern for the robot.
---
paper_title: Online Generation of Cyclic Leg Trajectories Synchronized with Sensor Measurement

paper_content:

The generation of trajectories for a biped robot is a problem which has been largely studied for several years, and many satisfying offline solutions exist for steady-state walking in absence of disturbances. The question is a little more complex when the generation of the desired trajectories of joints or links has to be achieved or adapted online, i.e. in real time, for example when it is wished to strongly synchronize these trajectories with an external motion. This is precisely the problem addressed in this paper. Indeed, we consider the case where the ''master'' motion is measured by a position sensor embedded on a human leg. We propose a method to synchronize the motion of a robot or of other device with respect to the output signal of the sensor. The main goal is to estimate as accurately as possible the current phase along the gait cycle. We use for that purpose a model based on a nonlinear oscillator, which we associate an observer. Introducing the sensor output in the observer allows us to compute the oscillator phase and to generate a synchronized multilinks trajectory, at a very low computational cost. The paper also presents evaluation results in terms of robustness against parameter estimation errors and velocity changes in the input.
---
paper_title: Modeling of a Neural Pattern Generator with Coupled nonlinear Oscillators

paper_content:

A set of van der Pol oscillators is arranged in a network in which each oscillator is coupled to each other oscillator. Through the selection of coupling coefficients, the network is made to appear as a ring and as a chain of coupled oscillators. Each oscillator is provided with amplitude, frequency, and offset parameters which have analytically indeterminable effects on the output waves. These systems are simulated on the digital computer in order to study the amplitude, frequency, offset, and phase relationships of the waves versus parameter changes. Based on the simulations, systems of coupled oscillators are configured so that they exhibit stable patterns of signals which can be used to model the central pattern generator (CPG) of living organisms. Using a simple biped as an example locomotory system, the CPG model generates control signals for simulated walking and jumping maneuvers. It is shown that with parameter adjustments, as guided by the simulations, the model can be made to generate kinematic trajectories which closely resemble those for the human walking gait. Further-more, minor tuning of these parameters along with some algebraic sign changes of coupling coefficients can effect a transition in the trajectories to those of a two-legged hopping gait. The generalized CPG model is shown to be versatile enough that it can also generate various n-legged gaits and spinal undulatory motions, as in the swimming motions of a fish.
---
paper_title: Coupled Van Der Pol oscillators utilised as Central pattern generators for quadruped locomotion

paper_content:

Central pattern generators (CPGs) inspired from neural system of animal are widely used for the control of locomotion in robots. The objective of this paper is to modeling a CPG-network formed by a set of mutually coupled Van Der Pol (VDP) oscillators to generating rhythmic movement patterns for multi-joint robot. Firstly, a VDP-CPG network is made up by four coupled VDP oscillators, which can produce multiple phase-locked oscillation patterns that correspond to the four basic quadrupedal gaits. Then the gait transitions between the different gaits are generated by altering the internal oscillator parameters. At last, we use VDP-CPG network to produce the joints trajectories for AIBO to control the locomotion. The simulation and experimental results demonstrate that the designed VDP-CPG network is effective to control the quadruped locomotion.
---
paper_title: Development of Adaptive Modular Active Leg (AMAL) using bipedal robotics technology

paper_content:

The objective of the work presented here is to develop a low cost active above knee prosthetic device exploiting bipedal robotics technology which will work utilizing the available biological motor control circuit properly integrated with a Central Pattern Generator (CPG) based control scheme. The approach is completely different from the existing Active Prosthetic devices, designed primarily as standalone systems utilizing multiple sensors and embedded rigid control schemes. In this research, first we designed a fuzzy logic based methodology for offering suitable gait pattern for an amputee, followed by formulating a suitable algorithm for designing a CPG, based on Rayleigh's oscillator. An indigenous probe, Humanoid Gait Oscillator Detector (HGOD) has been designed for capturing gait patterns from various individuals of different height, weight and age. These data are used to design a Fuzzy inference system which generates most suitable gait pattern for an amputee. The output of the Fuzzy inference system is used for designing a CPG best suitable for the amputee. We then developed a CPG based control scheme for calculating the damping profile in real time for maneuvering a prosthetic device called AMAL (Adaptive Modular Active Leg). Also a number of simulation results are presented which show the stable behavior of knee and hip angles and determine the stable limit cycles of the network.
---
paper_title: Fast Biped Walking with a Sensor-driven Neuronal Controller and Real-time Online Learning

paper_content:

In this paper, we present our design and experiments on a planar biped robot under the control of a pure sensor-driven controller. This design has some special mechanical features, for example small curved feet allowing rolling action and a properly positioned center of mass, that facilitate fast walking through exploitation of the robot's natural dynamics. Our sensor-driven controller is built with biologically inspired sensor- and motor-neuron models, and does not employ any kind of position or trajectory tracking control algorithm. Instead, it allows our biped robot to exploit its own natural dynamics during critical stages of its walking gait cycle. Due to the interaction between the sensor-driven neuronal controller and the properly designed mechanics of the robot, the biped robot can realize stable dynamic walking gaits in a large domain of the neuronal parameters. In addition, this structure allows the use of a policy gradient reinforcement learning algorithm to tune the parameters of the sensor-driven controller in real-time, during walking. This way RunBot can reach a relative speed of 3.5 leg lengths per second after only a few minutes of online learning, which is faster than that of any other biped robot, and is also comparable to the fastest relative speed of human walking.
---
paper_title: Design of central pattern generator for humanoid robot walking based on multi-objective GA

paper_content:

Recently, the field of humanoid robotics attracts more and more interest and the research on humanoid locomotion based on central pattern generators (CPG) reveals many challenging aspects. This paper describes the design of CPG for stable humanoid bipedal locomotion using an evolutionary approach. In this research, each joint of the humanoid is driven by a neuron that consists of two coupled neural oscillators, and corresponding joint's neurons are connected by strength weight. To achieve natural and robust walking pattern, an evolutionary-based multi-objective optimization algorithm is used to solve the weight optimization problem. The fitness functions are formulated based on zero moment point (ZMP), global attitude of the robot and the walking speed. In the algorithms, real value coding and tournament selection are applied, the crossover and mutation operators are chosen as heuristic crossover and boundary mutation respectively. Following evolving, the robot is able to walking in the given environment and a simulation shows the result.
---
paper_title: Pattern generators with sensory feedback for the control of quadruped locomotion

paper_content:

Central pattern generators (CPGs) are becoming a popular model for the control of locomotion of legged robots. Biological CPGs are neural networks responsible for the generation of rhythmic movements, especially locomotion. In robotics, a systematic way of designing such CPGs as artificial neural networks or systems of coupled oscillators with sensory feedback inclusion is still missing. In this contribution, we present a way of designing CPGs with coupled oscillators in which we can independently control the ascending and descending phases of the oscillations (i.e. the swing and stance phases of the limbs). Using insights from dynamical system theory, we construct generic networks of oscillators able to generate several gaits under simple parameter changes. Then we introduce a systematic way of adding sensory feedback from touch sensors in the CPG such that the controller is strongly coupled with the mechanical system it controls. Finally we control three different simulated robots (iCub, Aibo and Ghostdog) using the same controller to show the effectiveness of the approach. Our simulations prove the importance of independent control of swing and stance duration. The strong mutual coupling between the CPG and the robot allows for more robust locomotion, even under non precise parameters and non-flat environment.
---
paper_title: Dynamic walking and running of a bipedal robot using hybrid central pattern generator method

paper_content:

This paper presents simulation and experimental results of dynamic walking and running of a bipedal robot. We proposed the hybrid central pattern generator (H-CPG) method to realize adaptive dynamic motions including stepping and jumping. This method basically consisted of CPG models, but was added the force control system that controlled the acting force from a leg to a floor in the vertical and the horizontal directions separately. At this moment, 2D walking and running was realized on level ground and a slope with a speed of 1.6 m/s.
---
paper_title: Locomotion pattern generation of semi-looper type robots using central pattern generators based on van der Pol oscillators

paper_content:

A control problem is studied for generating the locomotion pattern of a semi-looper type robot by applying central pattern generators (CPGs), in which such a robot can realize two-rhythm motion and green caterpillar locomotion depending on the condition of environment. After deriving the dynamical model with two links and one actuator, the simulation of the robot is conducted using a CPG consisting of one van der Pol (VDP) oscillator. A CPG network composed of two VDP oscillators is further constructed to realize four-rhythm motion with three links and two actuators.
---
paper_title: Evolving Swimming Controllers for a Simulated Lamprey with Inspiration from Neurobiology

paper_content:

This paper presents how neural swimming controllers for a simulated lamprey can be developed using evolutionary algorithms. A genetic algorithm is used for evolving the architecture of a connectionist model which determines the muscular activity of a simulated body in interaction with water. This work is inspired by the biological model developed by Ekeberg which repro duces the central pattern generator observed in the real lamprey (Ekeberg, 1993). In evolving artificial controllers, we demonstrate that a genetic algorithm can be an interesting design tech nique for neural controllers and that there exist alternative solutions to the biological connectiv ity. A variety of neural controllers are evolved which can produce the pattern of oscillations necessary for swimming. These patterns can be modulated through the external excitation ap plied to the network in order to vary the speed and the direction of swimming. The best evolved controllers cover larger ranges of frequencies, phase lags and speeds of s...
---
paper_title: Design of a novel central pattern generator and the hebbian motion learning

paper_content:

In this paper, we propose a new CPG model and hebbian learning rule for the CPG. The output of the proposed CPG is determined by only phase differences of synchronization of component oscillators. A phase synchronization can be regarded as an adaptive behavior to an environment, so the CPG has an adaptavility despite of only simple connections to the environment. We also propose a motion learning rule for the proposed CPG. Since the rule is described by only simple signal processing, so that it can be realized by electric circuits easily, it will be able to be used efficiently for high degree of freedom robots.
---
paper_title: A Theory on Autonomous Distributed Systems with Application to a Gait Pattern Generator of Quadruped

paper_content:

This paper considers a synthesis approach to autonomous distributed systems in which the functional order of the entire system is generated by cooperative interaction among its subsystems, each of which has the autonomy to control a part of the state of the system, and its application to pattern generators of animal locomotion. First, biological locomotory rhythms and their generators, and gait patterns of quadrupeds, are reviewed briefly. Then, a design principle for autonomous coordination of many oscillators is proposed. Using these results, a gait pattern generator is synthesized. Finally, it is shown using computer simulations that the proposed systems generate desirable patterns.
---
paper_title: Configuring of Spiking Central Pattern Generator Networks for Bipedal Walking Using Genetic Algorthms

paper_content:

In limbed animals, spinal neural circuits responsible for controlling muscular activities during walking are called central pattern generators (CPG). CPG networks display oscillatory activities that actuates individual or groups of muscles in a coordinated fashion so that the limbs of the animal are flexed and extended at the appropriate time and with the required velocity for the animal to efficiently traverse various types of terrain, and to recover from environmental perturbation. Typically, the CPG networks are constructed with many neurons, each of which has a number of control parameters. As the number of muscles increases, it is often impossible to manually, albeit intelligently, select the network parameters for a particular movement. Furthermore, it is virtually impossible to reconfigure the parameters on-line. This paper describes how genetic algorithms (GA) can be used for on-line (re)configuring of CPG networks for a bipedal robot. We show that the neuron parameters and connection weights/network topology of a canonical walking network can be reconfigured within a few of generations of the GA. The networks, constructed with integrate-and-fire-with-adaptation (IFA) neurons, are implemented with a microcontroller and can be reconfigured to vary walking speed from 0.5Hz to 3.5Hz. The phase relationship between the hips and knees can be arbitrarily set (to within 1 degree) and prescribed complex joint angle profiles are realized. This is a powerful approach to generating complex muscle synergies for robots with multiple joints and distributed actuators.
---
paper_title: A central pattern generator for insect gait production

paper_content:

We present a neural network model inspired from both behavioral and neurophysiological data on insect locomotion. The model consists of a central rhythmic pattern generator and a sensory motor network. We show that it exhibits various behavioral properties observed in several insect species: it produces a continuum of stable gaits ranging from metachronal to tripod; the changes in the duration of protraction retraction phases for various walking speeds follow closely the behavorial data; the network is insensitive to the initial position of the legs, in that it can achieve rapidly a coherent phase relationship regardless of the initial conditions. In addition, this transient property also extends to situations where rapid changes occur in the walking speed; the network can reorganize its phase rapidly during fast accelerations as well as fast decelerations.
---
paper_title: Programmable central pattern generators: an application to biped locomotion control

paper_content:

We present a system of coupled nonlinear oscillators to be used as programmable central pattern generators, and apply it to control the locomotion of a humanoid robot. Central pattern generators are biological neural networks that can produce coordinated multidimensional rhythmic signals, under the control of simple input signals. They are found both in vertebrate and invertebrate animals for the control of locomotion. In this article, we present a novel system composed of coupled adaptive nonlinear oscillators that can learn arbitrary rhythmic signals in a supervised learning framework. Using adaptive rules implemented as differential equations, parameters such as intrinsic frequencies, amplitudes, and coupling weights are automatically adjusted to replicate a teaching signal. Once the teaching signal is removed, the trajectories remain embedded as the limit cycle of the dynamical system. An interesting aspect of this approach is that the learning is completely embedded into the dynamical system, and does not require external optimization algorithms. We use our system to encapsulate rhythmic trajectories for biped locomotion with a simulated humanoid robot, and demonstrate how it can be used to do online trajectory generation. The system can modulate the speed of locomotion, and even allow the reversal of direction (i.e. walking backwards). The integration of sensory feedback allows the online modulation of trajectories such as to increase the basin of stability of the gaits, and therefore the range of speeds that can be produced
---
paper_title: BoxyBot: a swimming and crawling fish robot controlled by a central pattern generator

paper_content:

We present a novel fish robot capable of swimming and crawling. The robot is driven by DC motors and has three actuated fins, with two pectoral fins and one caudal fin. It is loosely inspired from the boxfish. The control architecture of the robot is constructed around a central pattern generator (CPG) implemented as a system of coupled nonlinear oscillators, which, like its biological counterpart, can produce coordinated patterns of rhythmic activity while being modulated by simple control parameters. Using the CPG model, the robot is capable of performing and switching between a variety of different locomotor behaviors such as swimming forwards, swimming backwards, turning, rolling, moving upwards/downwards, and crawling. These behaviors are triggered and modulated by sensory input provided by light and water sensors. Results are presented demonstrating the agility of the robot, and interesting properties of a CPG-based control approach such as stability of the rhythmic patterns due to limit cycle behavior, and the production of smooth trajectories despite abrupt changes of control parameters
---
paper_title: Automated evolutionary design, robustness, and adaptation of sidewinding locomotion of a simulated snake-like robot

paper_content:

Inspired by the efficient method of locomotion of the rattlesnake Crotalus cerastes, the objective of this work is automatic design through genetic programming (GP) of the fastest possible (sidewinding) locomotion of simulated limbless, wheelless snake-like robot (Snakebot). The realism of simulation is ensured by employing the Open Dynamics Engine (ODE), which facilitates implementation of all physical forces, resulting from the actuators, joints constrains, frictions, gravity, and collisions. Reduction of the search space of the GP is achieved by representation of Snakebot as a system comprising identical morphological segments and by automatic definition of code fragments, shared among (and expressing the correlation between) the evolved dynamics of the vertical and horizontal turning angles of the actuators of Snakebot. Empirically obtained results demonstrate the emergence of sidewinding locomotion from relatively simple motion patterns of morphological segments. Robustness of the sidewinding Snakebot, which is considered to be the ability to retain its velocity when situated in an unanticipated environment, is illustrated by the ease with which Snakebot overcomes various types of obstacles such as a pile of or burial under boxes, rugged terrain, and small walls. The ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics is discussed. Discovering compensatory locomotion traits, Snakebot recovers completely from single damage and recovers a major extent of its original velocity when more significant damage is inflicted. Exploring the opportunity for automatic design and adaptation of a simulated artifact, this work could be considered as a step toward building real Snakebots, which are able to perform robustly in difficult environments.
---
paper_title: Experimentally Verified Optimal Serpentine Gait and Hyperredundancy of a Rigid-Link Snake Robot

paper_content:

In this study, we examine, for a six-link snake robot, how an optimal gait might change as a function of the snake- surface interaction model and how the overall locomotion performance changes under nonoptimal conditions such as joint failure. Simulations are evaluated for three different types of friction models, and it is shown that the gait parameters for serpentine motion are very dependant on the frictional model if minimum power expenditure is desired for a given velocity. Experimental investigations then motivate a surface interaction model not commonly used in snake locomotion studies. Using this new model, simulation results are compared to experiments for nominal and nonnominal locomotion cases including actuator faults. It is shown that this model quite accurately predicts locomotion velocities and link profiles, but that the accuracy of these predictions degrades severely at speeds where actuator dynamics become significant.
---
paper_title: Structural Evolution of Central Pattern Generators for Bipedal Walking in 3D Simulation

paper_content:

Anthropomorphic walking for a simulated bipedal robot has been realized by means of artificial evolution of central pattern generator (CPG) networks. The approach has been investigated through full rigid-body dynamics simulations in 3D of a bipedal robot with 14 degrees of freedom. The half-center CPG model has been used as an oscillator unit, with interconnection paths between oscillators undergoing structural modifications using a genetic algorithm. In addition, the connection weights in a feedback network of predefined structure were evolved. Furthermore, a supporting structure was added to the robot in order to guide the evolutionary process towards natural, human-like gaits. Subsequently, this structure was removed, and the ability of the best evolved controller to generate a bipedal gait without the help of the supporting structure was verified. Stable, natural gait patterns were obtained, with a maximum walking speed of around 0.9 m/s.
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paper_title: Passive compliant quadruped robot using Central Pattern Generators for locomotion control

paper_content:

We present a new quadruped robot, ldquoCheetahrdquo, featuring three-segment pantographic legs with passive compliant knee joints. Each leg has two degrees of freedom - knee and hip joint can be actuated using proximal mounted RC servo motors, force transmission to the knee is achieved by means of a bowden cable mechanism. Simple electronics to command the actuators from a desktop computer have been designed in order to test the robot. A Central Pattern Generator (CPG) network has been implemented to generate different gaits. A parameter space search was performed and tested on the robot to optimize forward velocity.
---
paper_title: Research on gait planning of artificial leg based on central pattern generator

paper_content:

Biped robot with heterogeneous legs (BRHL) is a novel robot model, which consists of an artificial leg and an intelligent bionic leg. The artificial leg is used to simulate the amputeepsilas healthy leg and the bionic leg works as the intelligent artificial limb. To describe the present gait of the healthy leg and make intelligent bionic leg follow the walking of artificial leg in all phases is the target of BRHLpsilas research. So gait planning of artificial leg is the emphasis of BRHLpsilas research. This paper uses the model of central pattern generator (CPG) in the research of artificial legpsilas gait planning from the point of biology. To obtain natural and robust walking pattern, genetic algorithm is used to optimize parameters of the CPG network model and the fitness function is formulated based on zero moment point (ZMP). Simulation results testify the feasibility of this method.
---
paper_title: Autonomous evolution of dynamic gaits with two quadruped robots

paper_content:

A challenging task that must be accomplished for every legged robot is creating the walking and running behaviors needed for it to move. In this paper we describe our system for autonomously evolving dynamic gaits on two of Sony's quadruped robots. Our evolutionary algorithm runs on board the robot and uses the robot's sensors to compute the quality of a gait without assistance from the experimenter. First, we show the evolution of a pace and trot gait on the OPEN-R prototype robot. With the fastest gait, the robot moves at over 10 m/min, which is more than forty body-lengths/min. While these first gaits are somewhat sensitive to the robot and environment in which they are evolved, we then show the evolution of robust dynamic gaits, one of which is used on the ERS-110, the first consumer version of AIBO.
---
paper_title: Design of a Central Pattern Generator for Bionic-robot Joint with Angular Frequency Modulation

paper_content:

The paper proposes an artificial central pattern generator (CPG) for bionic-robot joint control. The neural oscillator adopted to produce rhythmic pattern is specially designed from original sin-cosine oscillator model. An amplitude neural estimator consisted of two neurons is presented to provide sensor feedback to CPG control. The artificial CPG can adapt itself to the physical system parameters variety by rhythmic movement angular frequency modulation.
---
paper_title: Learning CPG-based Biped Locomotion with a Policy Gradient Method: Application to a Humanoid Robot

paper_content:

In this paper we describe a learning framework for a central pattern generator (CPG)-based biped locomotion controller using a policy gradient method. Our goals in this study are to achieve CPG-based biped walking with a 3D hardware humanoid and to develop an efficient learning algorithm with CPG by reducing the dimensionality of the state space used for learning. We demonstrate that an appropriate feedback controller can be acquired within a few thousand trials by numerical simulations and the controller obtained in numerical simulation achieves stable walking with a physical robot in the real world. Numerical simulations and hardware experiments evaluate the walking velocity and stability. The results suggest that the learning algorithm is capable of adapting to environmental changes. Furthermore, we present an online learning scheme with an initial policy for a hardware robot to improve the controller within 200 iterations.
---
paper_title: Bipedal locomotion control using a four-compartmental central pattern generator

paper_content:

In this paper, we develop a simple bipedal locomotion algorithm based on biological concepts. The algorithm utilized a central pattern generator (CPG) composed of four coupled neural oscillators (NO) to generate control signal for the bipedal robot. Feedbacks from the robot dynamics and the environment are used to update the CPG online. Our algorithm is then tested on a seven link model of bipedal robot. Simulation results suggest the proposed CPG can generate a smooth and continuous walking pattern for the robot.
---
paper_title: Online Generation of Cyclic Leg Trajectories Synchronized with Sensor Measurement

paper_content:

The generation of trajectories for a biped robot is a problem which has been largely studied for several years, and many satisfying offline solutions exist for steady-state walking in absence of disturbances. The question is a little more complex when the generation of the desired trajectories of joints or links has to be achieved or adapted online, i.e. in real time, for example when it is wished to strongly synchronize these trajectories with an external motion. This is precisely the problem addressed in this paper. Indeed, we consider the case where the ''master'' motion is measured by a position sensor embedded on a human leg. We propose a method to synchronize the motion of a robot or of other device with respect to the output signal of the sensor. The main goal is to estimate as accurately as possible the current phase along the gait cycle. We use for that purpose a model based on a nonlinear oscillator, which we associate an observer. Introducing the sensor output in the observer allows us to compute the oscillator phase and to generate a synchronized multilinks trajectory, at a very low computational cost. The paper also presents evaluation results in terms of robustness against parameter estimation errors and velocity changes in the input.
---
paper_title: Modeling of a Neural Pattern Generator with Coupled nonlinear Oscillators

paper_content:

A set of van der Pol oscillators is arranged in a network in which each oscillator is coupled to each other oscillator. Through the selection of coupling coefficients, the network is made to appear as a ring and as a chain of coupled oscillators. Each oscillator is provided with amplitude, frequency, and offset parameters which have analytically indeterminable effects on the output waves. These systems are simulated on the digital computer in order to study the amplitude, frequency, offset, and phase relationships of the waves versus parameter changes. Based on the simulations, systems of coupled oscillators are configured so that they exhibit stable patterns of signals which can be used to model the central pattern generator (CPG) of living organisms. Using a simple biped as an example locomotory system, the CPG model generates control signals for simulated walking and jumping maneuvers. It is shown that with parameter adjustments, as guided by the simulations, the model can be made to generate kinematic trajectories which closely resemble those for the human walking gait. Further-more, minor tuning of these parameters along with some algebraic sign changes of coupling coefficients can effect a transition in the trajectories to those of a two-legged hopping gait. The generalized CPG model is shown to be versatile enough that it can also generate various n-legged gaits and spinal undulatory motions, as in the swimming motions of a fish.
---
paper_title: Coupled Van Der Pol oscillators utilised as Central pattern generators for quadruped locomotion

paper_content:

Central pattern generators (CPGs) inspired from neural system of animal are widely used for the control of locomotion in robots. The objective of this paper is to modeling a CPG-network formed by a set of mutually coupled Van Der Pol (VDP) oscillators to generating rhythmic movement patterns for multi-joint robot. Firstly, a VDP-CPG network is made up by four coupled VDP oscillators, which can produce multiple phase-locked oscillation patterns that correspond to the four basic quadrupedal gaits. Then the gait transitions between the different gaits are generated by altering the internal oscillator parameters. At last, we use VDP-CPG network to produce the joints trajectories for AIBO to control the locomotion. The simulation and experimental results demonstrate that the designed VDP-CPG network is effective to control the quadruped locomotion.
---
paper_title: A Biologically Inspired Biped Locomotion Strategy for Humanoid Robots: Modulation of Sinusoidal Patterns by a Coupled Oscillator Model

paper_content:

Biological systems seem to have a simpler but more robust locomotion strategy than that of the existing biped walking controllers for humanoid robots. We show that a humanoid robot can step and walk using simple sinusoidal desired joint trajectories with their phase adjusted by a coupled oscillator model. We use the center-of-pressure location and velocity to detect the phase of the lateral robot dynamics. This phase information is used to modulate the desired joint trajectories. We do not explicitly use dynamical parameters of the humanoid robot. We hypothesize that a similar mechanism may exist in biological systems. We applied the proposed biologically inspired control strategy to our newly developed human-sized humanoid robot computational brain (CB) and a small size humanoid robot, enabling them to generate successful stepping and walking patterns.
---
paper_title: Development of Adaptive Modular Active Leg (AMAL) using bipedal robotics technology

paper_content:

The objective of the work presented here is to develop a low cost active above knee prosthetic device exploiting bipedal robotics technology which will work utilizing the available biological motor control circuit properly integrated with a Central Pattern Generator (CPG) based control scheme. The approach is completely different from the existing Active Prosthetic devices, designed primarily as standalone systems utilizing multiple sensors and embedded rigid control schemes. In this research, first we designed a fuzzy logic based methodology for offering suitable gait pattern for an amputee, followed by formulating a suitable algorithm for designing a CPG, based on Rayleigh's oscillator. An indigenous probe, Humanoid Gait Oscillator Detector (HGOD) has been designed for capturing gait patterns from various individuals of different height, weight and age. These data are used to design a Fuzzy inference system which generates most suitable gait pattern for an amputee. The output of the Fuzzy inference system is used for designing a CPG best suitable for the amputee. We then developed a CPG based control scheme for calculating the damping profile in real time for maneuvering a prosthetic device called AMAL (Adaptive Modular Active Leg). Also a number of simulation results are presented which show the stable behavior of knee and hip angles and determine the stable limit cycles of the network.
---
paper_title: Fast Biped Walking with a Sensor-driven Neuronal Controller and Real-time Online Learning

paper_content:

In this paper, we present our design and experiments on a planar biped robot under the control of a pure sensor-driven controller. This design has some special mechanical features, for example small curved feet allowing rolling action and a properly positioned center of mass, that facilitate fast walking through exploitation of the robot's natural dynamics. Our sensor-driven controller is built with biologically inspired sensor- and motor-neuron models, and does not employ any kind of position or trajectory tracking control algorithm. Instead, it allows our biped robot to exploit its own natural dynamics during critical stages of its walking gait cycle. Due to the interaction between the sensor-driven neuronal controller and the properly designed mechanics of the robot, the biped robot can realize stable dynamic walking gaits in a large domain of the neuronal parameters. In addition, this structure allows the use of a policy gradient reinforcement learning algorithm to tune the parameters of the sensor-driven controller in real-time, during walking. This way RunBot can reach a relative speed of 3.5 leg lengths per second after only a few minutes of online learning, which is faster than that of any other biped robot, and is also comparable to the fastest relative speed of human walking.
---
paper_title: Design of central pattern generator for humanoid robot walking based on multi-objective GA

paper_content:

Recently, the field of humanoid robotics attracts more and more interest and the research on humanoid locomotion based on central pattern generators (CPG) reveals many challenging aspects. This paper describes the design of CPG for stable humanoid bipedal locomotion using an evolutionary approach. In this research, each joint of the humanoid is driven by a neuron that consists of two coupled neural oscillators, and corresponding joint's neurons are connected by strength weight. To achieve natural and robust walking pattern, an evolutionary-based multi-objective optimization algorithm is used to solve the weight optimization problem. The fitness functions are formulated based on zero moment point (ZMP), global attitude of the robot and the walking speed. In the algorithms, real value coding and tournament selection are applied, the crossover and mutation operators are chosen as heuristic crossover and boundary mutation respectively. Following evolving, the robot is able to walking in the given environment and a simulation shows the result.
---
paper_title: Pattern generators with sensory feedback for the control of quadruped locomotion

paper_content:

Central pattern generators (CPGs) are becoming a popular model for the control of locomotion of legged robots. Biological CPGs are neural networks responsible for the generation of rhythmic movements, especially locomotion. In robotics, a systematic way of designing such CPGs as artificial neural networks or systems of coupled oscillators with sensory feedback inclusion is still missing. In this contribution, we present a way of designing CPGs with coupled oscillators in which we can independently control the ascending and descending phases of the oscillations (i.e. the swing and stance phases of the limbs). Using insights from dynamical system theory, we construct generic networks of oscillators able to generate several gaits under simple parameter changes. Then we introduce a systematic way of adding sensory feedback from touch sensors in the CPG such that the controller is strongly coupled with the mechanical system it controls. Finally we control three different simulated robots (iCub, Aibo and Ghostdog) using the same controller to show the effectiveness of the approach. Our simulations prove the importance of independent control of swing and stance duration. The strong mutual coupling between the CPG and the robot allows for more robust locomotion, even under non precise parameters and non-flat environment.
---
paper_title: Gait adaptation method of biped robot for various terrains using central pattern generator (CPG) and learning mechanism

paper_content:

There is evidence showing that animals have inherent rhythmic pattern generators called central pattern generator (CPG) that produce locomotion, respiration, heartbeat, and etc. Until now, the CPG has been widely used in robotic systems especially for locomotion. In this paper, we propose a gait adaptation method of biped robot for various terrains. The CPGs are used for creating desired joint angle of a biped robot. And a learning mechanism is realized with genetic algorithms (GAs) and Neural Network (NN). From an each terrain, the most suitable parameters of CPGs that can produce stable biped robot gait are founded by GAs. The parameter set founded by GAs and sensor data at that time are used for training the NN. After finishing training of the NN, the NN can be used for producing suitable parameters of the CPGs according to a sensor input. Finally the gait of the biped robot is changed according to environment.
---
paper_title: Locomotion pattern generation of semi-looper type robots using central pattern generators based on van der Pol oscillators

paper_content:

A control problem is studied for generating the locomotion pattern of a semi-looper type robot by applying central pattern generators (CPGs), in which such a robot can realize two-rhythm motion and green caterpillar locomotion depending on the condition of environment. After deriving the dynamical model with two links and one actuator, the simulation of the robot is conducted using a CPG consisting of one van der Pol (VDP) oscillator. A CPG network composed of two VDP oscillators is further constructed to realize four-rhythm motion with three links and two actuators.
---
paper_title: Dynamically balanced optimal gaits of a ditch-crossing biped robot

paper_content:

This paper deals with the generation of dynamically balanced gaits of a ditch-crossing biped robot having seven degrees of freedom (DOFs). Three different approaches, namely analytical, neural network (NN)-based and fuzzy logic (FL)-based, have been developed to solve the said problem. The former deals with the analytical modeling of the ditch-crossing gait of a biped robot, whereas the latter two approaches aim to maximize the dynamic balance margin of the robot and minimize the power consumption during locomotion, after satisfying a constraint stating that the changes of joint torques should lie within a pre-specified value to ensure its smooth walking. It is to be noted that the power consumption and dynamic balance of the robot are also dependent on the position of the masses on various links and the trajectory followed by the hip joint. A genetic algorithm (GA) is used to provide training off-line, to the NN-based and FL-based gait planners developed. Once optimized, the planners will be able to generate the optimal gaits on-line. Both the NN-based and FL-based gait planners are able to generate more balanced gaits and that, too, at the cost of lower power consumption compared to those yielded by the analytical approach. The NN-based and FL-based approaches are found to be more adaptive compared to the other approach in generating the gaits of the biped robot.
---
paper_title: Application of Genetic Algorithms for biped robot gait synthesis optimization during walking and going up-stairs

paper_content:

Selecting an appropriate gait can reduce consumed energy by a biped robot. In this paper, a Genetic Algorithm gait synthesis method is proposed, which generates the angle trajectories based on the minimum consumed energy and minimum torque change. The gait synthesis is considered for two cases: walking and going up-stairs. The proposed method can be applied for a wide range of step lengths and step times during walking; or step lengths, stair heights and step times for going up-stairs. The angle trajectories are generated without neglecting the stability of the biped robot. The angle trajectories can be generated for other tasks to be performed by the biped robot, like going down-stairs, overcoming obstacles, etc. In order to verify the effectiveness of the proposed method, the results for minimum consumed energy and minimum torque change are compared. A Radial Basis Function Neural Network is considered for the real-time application. Simulations are realized based upon the parameters of the 'Bonten-Maru ...
---
paper_title: Real time gait generation for autonomous humanoid robots: A case study for walking

paper_content:

Abstract As autonomous humanoid robots assume more important roles in everyday life, they are expected to perform many different tasks and quickly adapt to unknown environments. Therefore, humanoid robots must generate quickly the appropriate gait based on information received from visual system. In this work, we present a new method for real time gait generation during walking based on Neural Networks. The minimum consumed energy gaits similar with human motion, are used to teach the Neural Network. After supervised learning, the Neural Network can quickly generate the humanoid robot gait. Simulation and experimental results utilizing the “Bonten-Maru I” humanoid robot show good performance of the proposed method.
---
paper_title: A Learning Architecture Based on Reinforcement Learning for Adaptive Control of the Walking Machine LAURON

paper_content:

The learning of complex control behaviour of autonomous mobile robots is one of the actual research topics. In this article an intelligent control architecture is presented which integrates learning methods and available domain knowledge. This control architecture is based on Reinforcement Learning and allows continuous input and output parameters, hierarchical learning, multiple goals, self-organized topology of the used networks and online learning. As a testbed this architecture is applied to the six-legged walking machine LAURON to learn leg control and leg coordination.
---
paper_title: Control Strategy for the Robust Dynamic Walk of a Biped Robot

paper_content:

This paper presents the main simulation and experimental results from studies of the robustness of a proposed new control strategy for the under-actuated robot RABBIT. The disturbances studied include modifications of the characteristics of the foot/ground interaction and the application of external forces to the trunk of the robot. These two kinds of disturbance correspond to those most frequently encountered by biped robots during walking in an unknown environment.
---
paper_title: Robustness of the dynamic walk of a biped robot subjected to disturbing external forces by using CMAC neural networks

paper_content:

Abstract In this paper, we propose a control strategy allowing us to perform the dynamic walking gait of an under-actuated robot even if this one is subjected to destabilizing external disturbances. This control strategy is based on two stages. The first one consists of using a set of pragmatic rules in order to generate a succession of passive and active phases allowing us to perform a dynamic walking gait of the robot. The joint trajectories of this reference gait are learned by using neural networks. In the second stage, we use these neural networks to generate the learned trajectories during the first stage. The goal of the use of these neural networks is to increase the robustness of the control of the dynamic walking gait of this robot in the case of external disturbances. The first experimental results are also presented.
---
paper_title: Biped dynamic walking using reinforcement learning

paper_content:

This paper presents some results from a study of biped dynamic walking using reinforcement learning. During this study a hardware biped robot was built, a new reinforcement learning algorithm as well as a new learning architecture were developed. The biped learned dynamic walking without any previous knowledge about its dynamic model. The self scaling reinforcement (SSR) learning algorithm was developed in order to deal with the problem of reinforcement learning in continuous action domains. The learning architecture was developed in order to solve complex control problems. It uses different modules that consist of simple controllers and small neural networks. The architecture allows for easy incorporation of new modules that represent new knowledge, or new requirements for the desired task.
---
paper_title: Neural networks for the control of a six-legged walking machine

paper_content:

Abstract In this paper a hierarchical control architecture for a six-legged walking machine is presented. The basic components of this architecture are neural networks which are taught in using examples of the control process. It is shown how the basic components “leg control” and “leg coordination” have been implemented by recurrent and feedforward networks respectively. The teaching process and the tests of the walking behaviour have mainly been done in a simulation system. First tests of the leg control on our real walking machine LAURON are also described.
---
paper_title: A movement pattern generator model using artificial neural networks

paper_content:

The authors have developed a movement pattern generator, using an artificial neural network (ANN) for generating periodic movement trajectories. This model is based on the concept of 'central pattern generators'. Jordan's (1986) sequential network, which is capable of learning sequences of patterns, was modified and used to generate several bipedal trajectories (or gaits), coded in task space, at different frequencies. The network model successfully learned all of the trajectories presented to it. The model has many attractive properties, such as limit cycle behavior, generalization of trajectories and frequencies, phase maintenance, and fault tolerance. The movement pattern generator model is potentially applicable for improved understanding of animal locomotion and for use in legged robots and rehabilitation medicine. >
---
paper_title: Evolution of Central Pattern Generators for Bipedal Walking in a Real-Time Physics Environment

paper_content:

We describe an evolutionary approach to the control problem of bipedal walking. Using a full rigid-body simulation of a biped, it was possible to evolve recurrent neural networks that controlled stable straight-line walking on a planar surface. No proprioceptive information was necessary in order to achieve this task. Furthermore, simple sensory input to locate a sound source was integrated to achieve directional walking. To our knowledge, this is the first work that demonstrates the application of evolutionary optimization to 3D physically simulated biped locomotion.
---
paper_title: Recurrent neural networks as pattern generators

paper_content:

A fully connected recurrent ANN model is proposed as a generator of stable limit cycles. A hybrid genetic algorithm is used for training the network model. The behaviour of the model is presented through a number of simulation experiments and the stability of the generated limit cycles is tested under several conditions. >
---
paper_title: Evolving Dynamical Neural Networks for Adaptive Behavior

paper_content:

We would like the behavior of the artificial agents that we construct to be as well-adapted to their environments as natural animals are to theirs. Unfortunately, designing controllers with these properties is a very difficult task. In this article, we demonstrate that continuous-time recurrent neural networks are a viable mechanism for adaptive agent control and that the genetic algorithm can be used to evolve effective neural controllers. A significant advantage of this approach is that one need specify only a measure of an agent's overall performance rather than the precise motor output trajectories by which it is achieved. By manipulating the performance evaluation, one can place selective pressure on the development of controllers with desired properties. Several novel controllers have been evolved, including a chemotaxis controller that switches between different strategies depending on environmental conditions, and a locomotion controller that takes advantage of sensory feedback if available but th...
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paper_title: Application of evolved locomotion controllers to a hexapod robot

paper_content:

In previous work, we demonstrated that genetic algorithms could be used to evolve dynamical neural networks for controlling the locomotion of a simulated hexapod agent. We also demonstrated that these evolved controllers were robust to loss of sensory feedback and other peripheral variations. In this paper, we show that these locomotion controllers, evolved in simulation, are capable of directing the walking of a real six-legged robot, and that many of the desirable properties observed in simulation carry over directly to the real world. In addition, we demonstrate that these controllers are amenable to hardware implementation and can thus be easily embodied within the robot.
---
paper_title: Design of a Central Pattern Generator Using Reservoir Computing for Learning Human Motion

paper_content:

To generate coordinated periodic movements, robot locomotion demands mechanisms which are able to learn and produce stable rhythmic motion in a controllable way. Because systems based on biological central pattern generators (CPGs) can cope with these demands, these kind of systems are gaining in success. In this work we introduce a novel methodology that uses the dynamics of a randomly connected recurrent neural network for the design of CPGs. When a randomly connected recurrent neural network is excited with one or more useful signals, an output can be trained by learning an instantaneous linear mapping of the neuron states. This technique is known as reservoir computing (RC). We will show that RC has the necessary capabilities to be fruitful in designing a CPG that is able to learn human motion which is applicable for imitation learning in humanoid robots.
---
paper_title: Efference copies in neural control of dynamic biped walking

paper_content:

In the early 1950s, von Holst and Mittelstaedt proposed that motor commands copied within the central nervous system (efference copy) help to distinguish 'reafference' activity (afference activity due to self-generated motion) from 'exafference' activity (afference activity due to external stimulus). In addition, an efference copy can be also used to compare it with the actual sensory feedback in order to suppress self-generated sensations. Based on these biological findings, we conduct here two experimental studies on our biped ''RunBot'' where such principles together with neural forward models are applied to RunBot's dynamic locomotion control. The main purpose of this article is to present the modular design of RunBot's control architecture and discuss how the inherent dynamic properties of the different modules lead to the required signal processing. We believe that the experimental studies pursued here will sharpen our understanding of how the efference copies influence dynamic locomotion control to the benefit of modern neural control strategies in robots.
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paper_title: Neuroethological Concepts and their Transfer to Walking Machines

paper_content:

A systems approach to animal motor behavior reveals concepts that can be useful for the pragmatic design of walking machines. This is because the relation of animal behavior to its underlying nervous control algorithms bears many parallels to the relation of machine function to electronic control. Here, three major neuroethological concepts of motor behavior are described in terms of a conceptual framework based on artificial neural networks (ANN). Central patterns of activity and postural reflexes are both interpreted as a result of feedback loops, with the distinction of loops via an internal model from loops via the physical environment (body, external world). This view allows continuous transitions between predictive (centrally driven) and reactive (reflex driven) motor systems. Motor primitives, behavioral modules that are elicited by distinct commands, are also considered. ANNs capture these three major concepts in terms of a formal description, in which the interactions and mutual interdependences ...
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paper_title: Probabilistic Balance Monitoring for Bipedal Robots

paper_content:

In this paper, a probability-based balance monitoring concept for humanoid robots is proposed. Two algorithms are presented that allow us to distinguish between exceptional situations and normal operations. The first classification approach uses Gaussian-Mixture-Models (GMM) to describe the distribution of the robot's sensor data for typical situations such as stable walking or falling down. With the GMM it is possible to state the probability of the robot being in one of the known situations. The concept of the second algorithm is based on Hidden-Markov-Models (HMM). The objective is to detect and classify unstable situations by means of their typical sequences in the robot's sensor data. When appropriate reflex motions are linked to the critical situations, the robot can prevent most falls or is at least able to execute a controlled falling motion. The proposed algorithms are verified by simulations and experiments with our bipedal robot BARt-UH.
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paper_title: Embodied Symbol Emergence Based on Mimesis Theory

paper_content:

“Mimesis” theory focused in the cognitive science field and “mirror neurons” found in the biology field show that the behavior generation process is not independent of the behavior cognition process. The generation and cognition processes have a close relationship with each other. During the behavioral imitation period, a human being does not practice simple joint coordinate transformation, but will acknowledge the parents’ behavior. It understands the behavior after abstraction as symbols, and will generate its self-behavior. Focusing on these facts, we propose a new method which carries out the behavior cognition and behavior generation processes at the same time. We also propose a mathematical model based on hidden Markov models in order to integrate four abilities: (1) symbol emergence; (2) behavior recognition; (3) self-behavior generation; (4) acquiring the motion primitives. Finally, the feasibility of this method is shown through several experiments on a humanoid robot.
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paper_title: Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains

paper_content:

This paper describes a novel approach for autonomous and incremental learning of motion pattern primitives by observation of human motion. Human motion patterns are abstracted into a dynamic stochastic model, which can be used for both subsequent motion recognition and generation, analogous to the mirror neuron hypothesis in primates. The model size is adaptable based on the discrimination requirements in the associated region of the current knowledge base. A new algorithm for sequentially training the Markov chains is developed, to reduce the computation cost during model adaptation. As new motion patterns are observed, they are incrementally grouped together using hierarchical agglomerative clustering based on their relative distance in the model space. The clustering algorithm forms a tree structure, with specialized motions at the tree leaves, and generalized motions closer to the root. The generated tree structure will depend on the type of training data provided, so that the most specialized motions will be those for which the most training has been received. Tests with motion capture data for a variety of motion primitives demonstrate the efficacy of the algorithm.
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paper_title: Mimesis Model from Partial Observations for a Humanoid Robot

paper_content:

This paper proposes a new mimesis scheme for partial observations, consisting of two strategies; (1) motion understanding from partial observations and (2) proto-symbol-based motion duplication. With the proposed method, whole-body motion imitation is possible even when observing partial motion data. The scheme enables a humanoid robot to imitate a new observed motion by utilizing its own prior knowledge, without learning the demonstrated motion. Evaluation factors, such as inheritance coordinate and matching error, are introduced to evaluate imitation performance. The feasibility of the proposed scheme is demonstrated by an evaluation for a 20-degree-of-freedom humanoid robot.
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paper_title: Experiments in learning distributed control for a hexapod robot

paper_content:

Abstract This paper reports on experiments involving a hexapod robot. Motivated by neurobiological evidence that control in real hexapod insects is distributed leg-wise, we investigated two approaches to learning distributed controllers: genetic algorithms and reinforcement learning. In the case of reinforcement learning, a new learning algorithm was developed to encourage cooperation between legs. Results from both approaches are presented and compared.
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paper_title: Gait Optimization through Search

paper_content:

We present a search-based method for the generation of a terrain-adaptive optimal gait of a six-legged walking machine. In this, several heuristic rules have been proposed to reduce the search effort. We identify the useful support states of the machine and form a table to indicate for each of these states the list of other states to which a transition can be made. This helps in converging to and maintaining a periodic gait through a limited search while retaining adequate options to deviate from such a gait as and when needed. The criterion for optimization is coded into a function that evaluates the promise of a node in the search graph. We have shown how this function may be designed to generate the common periodic gaits like the wave gait, the equal phase gait, and the follow-the-leader gait. The purpose is to demonstrate that the proposed method is sufficiently general and can cater to a wide range of optimizing requirements.
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paper_title: Dynamically balanced optimal gaits of a ditch-crossing biped robot

paper_content:

This paper deals with the generation of dynamically balanced gaits of a ditch-crossing biped robot having seven degrees of freedom (DOFs). Three different approaches, namely analytical, neural network (NN)-based and fuzzy logic (FL)-based, have been developed to solve the said problem. The former deals with the analytical modeling of the ditch-crossing gait of a biped robot, whereas the latter two approaches aim to maximize the dynamic balance margin of the robot and minimize the power consumption during locomotion, after satisfying a constraint stating that the changes of joint torques should lie within a pre-specified value to ensure its smooth walking. It is to be noted that the power consumption and dynamic balance of the robot are also dependent on the position of the masses on various links and the trajectory followed by the hip joint. A genetic algorithm (GA) is used to provide training off-line, to the NN-based and FL-based gait planners developed. Once optimized, the planners will be able to generate the optimal gaits on-line. Both the NN-based and FL-based gait planners are able to generate more balanced gaits and that, too, at the cost of lower power consumption compared to those yielded by the analytical approach. The NN-based and FL-based approaches are found to be more adaptive compared to the other approach in generating the gaits of the biped robot.
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paper_title: Integration of linguistic and numerical information for biped control

paper_content:

Bipedal locomotion is an important hallmark of human evolution. Despite of complex control systems, human locomotion is characterized by smooth, regular, and repeating movements. Therefore, there is the potential for applying human locomotion strategies and any knowledge available to the biped control. In order to make the most use of the information available, a linguistic-numerical integration-based biped control method is proposed in this paper. The numerical data from biped measuring instruments, and the linguistic rules obtained from intuitive walking knowledge and biomechanics study have been classified into four categories: direct rules, direct data, indirect rules, and indirect data. Based on inverse learning and data fusion theory, two simple and intuitive integration schemes are proposed to integrate linguistic and numerical information with various forms, such as direct and indirect. One is neurofuzzy-based integration, and another is fuzzy rules extraction-based integration. The simulation results show that the biped gait and joint control performance can be significantly improved by the prescribed synergy method-based neurofuzzy gait synthesis and fuzzy rules extraction-based joint control strategies using linguistic and numerical integrated information.
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paper_title: Robot learning with GA-based fuzzy reinforcement learning agents

paper_content:

Abstract How to learn from both expert knowledge and measurement-based information for a robot to acquire perception and motor skills is a challenging research topic in the field of autonomous robotic systems. For this reason, a general GA (genetic algorithm)-based fuzzy reinforcement learning (GAFRL) agent is proposed in this paper. We first characterize the robot learning problem and point out some major issues that need to be addressed in conjunction with reinforcement learning. Based on a neural fuzzy network architecture of the GAFRL agent, we then discuss how different kinds of expert knowledge and measurement-based information can be incorporated in the GAFRL agent so as to accelerate its learning. By making use of the global optimization capability of GAs, the GAFRL can solve the local minima problem in traditional actor-critic reinforcement learning. On the other hand, with the prediction capability of the critic network, GAs can evaluate the candidate solutions regularly even during the periods without external feedback from the environment. This can guide GAs to perform a more effective global search. Finally, different types of GAFRL agents are constructed and verified using the simulation model of a physical biped robot.
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paper_title: Fuzzy-logic zero-moment-point trajectory generation for reduced trunk motions of biped robots

paper_content:

Trunk motions are typically used in biped robots to stabilize the locomotion. However, they can be very large for some leg trajectories unless they are carefully designed. This paper proposes a fuzzy-logic zero-moment-point (ZMP) trajectory generator that would eventually reduce the swing motion of the trunk significantly even though the leg trajectory is casually designed, for example, simply to avoid obstacles. The fuzzy-logic ZMP trajectory generator uses the leg trajectory as an input. The resulting ZMP trajectory is similar to that of a human one and continuously moves forward in the direction of the locomotion. The trajectory of the trunk to stabilize the locomotion is determined by solving a differential equation with the ZMP trajectory and the leg trajectory known. The proposed scheme is simulated on a 7-DOF biped robot in the sagittal plane. The simulation results show that the ZMP trajectory generated by the proposed fuzzy-logic generator increases the stability of the locomotion and thus reduces the motion range of the trunk significantly.
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paper_title: Dynamic balance of a biped robot using fuzzy reinforcement learning agents

paper_content:

This paper presents a general fuzzy reinforcement learning (FRL) method for biped dynamic balance control. Based on a neuro fuzzy network architecture, different kinds of expert knowledge and measurement-based information can be incorporated into the FRL agent to initialise its action network, critic network and/or evaluation feedback module so as to aecelerate its learning. The proposed FRL agent is constructed and verified using the simulation model of a physical biped robot. The sinmtation analysis shows that by incorporation of the human intuitive balancing knowledge and walking evaluation knowledge, the FRL agent's learning rate for side-to-side and front-to-back balance of the simulated biped can be improved. We also demonstrate that it is possible for a biped robot to start its walking with a priori knowledge and then learn to improve its behaviour with the FRL agents.
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paper_title: Optimal Path and Gait Generations Simultaneously of a Six-legged Robot Using a GA-Fuzzy Approach

paper_content:

This paper describes a new method for generating optimal path and gait simultaneously of a six-legged robot using a combined GA-fuzzy approach. The problem of combined path and gait generations involves three steps, namely determination of vehicle’s trajectory, foothold selection and design of a sequence of leg movements. It is a complicated task and no single traditional approach is found to be successful in handling this problem. Moreover, the traditional approaches do not consider optimization issues, yet they are computationally expensive. Thus, the generated path and gaits may not be optimal in any sense. To solve such problems optimally, there is still a need for the development of an efficient and computationally faster algorithm. In the proposed genetic-fuzzy approach, optimal path and gaits are generated by using fuzzy logic controllers (FLCs) and genetic algorithms (GAs) are used to find optimized FLCs. The optimization is done off-line on a number of training scenarios and optimal FLCs are found. The hexapod can then use these GA-tuned FLCs to navigate in test-case scenarios.
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paper_title: On-line stable gait generation of a two-legged robot using a genetic–fuzzy system

paper_content:

Abstract Gait generation for legged vehicles has since long been considered as an area of keen interest by the researchers. Soft computing is an emerging technique, whose utility is more stressed, when the problems are ill-defined, difficult to model and exhibit large scale solution spaces. Gait generation for legged vehicles is a complex task. Therefore, soft computing can be applied to solve it. In this work, gait generation problem of a two-legged robot is modeled using a fuzzy logic controller (FLC), whose rule base is optimized offline, using a genetic algorithm (GA). Two different GA-based approaches (to improve the performance of FLC) are developed and their performances are compared to that of manually constructed FLC. Once optimized, the FLCs will be able to generate dynamically stable gait of the biped. As the CPU-time of the algorithm is found to be only 0.002 s in a P-III PC, the algorithm is suitable for on-line (real-time) implementations.
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paper_title: Incremental Learning and Memory Consolidation of Whole Body Human Motion Primitives

paper_content:

The ability to learn during continuous and on-line observation would be advantageous for humanoid robots, as it would enable them to learn during co-location and interaction in the human environment. However, when motions are being learned and clustered on-line, there is a trade-off between classification accuracy and the number of training examples, resulting in potential misclassifications both at the motion and hierarchy formation level. This article presents an approach enabling fast on-line incremental learning, combined with an incremental memory consolidation process correcting initial misclassifications and errors in organization, to improve the stability and accuracy of the learned motions, analogous to the memory consolidation process following motor learning observed in humans. Following initial organization, motions are randomly selected for reclassification, at both low and high levels of the hierarchy. If a better reclassification is found, the knowledge structure is reorganized to comply. The approach is validated during incremental acquisition of a motion database containing a variety of full body motions.1
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paper_title: Dynamically balanced optimal gaits of a ditch-crossing biped robot

paper_content:

This paper deals with the generation of dynamically balanced gaits of a ditch-crossing biped robot having seven degrees of freedom (DOFs). Three different approaches, namely analytical, neural network (NN)-based and fuzzy logic (FL)-based, have been developed to solve the said problem. The former deals with the analytical modeling of the ditch-crossing gait of a biped robot, whereas the latter two approaches aim to maximize the dynamic balance margin of the robot and minimize the power consumption during locomotion, after satisfying a constraint stating that the changes of joint torques should lie within a pre-specified value to ensure its smooth walking. It is to be noted that the power consumption and dynamic balance of the robot are also dependent on the position of the masses on various links and the trajectory followed by the hip joint. A genetic algorithm (GA) is used to provide training off-line, to the NN-based and FL-based gait planners developed. Once optimized, the planners will be able to generate the optimal gaits on-line. Both the NN-based and FL-based gait planners are able to generate more balanced gaits and that, too, at the cost of lower power consumption compared to those yielded by the analytical approach. The NN-based and FL-based approaches are found to be more adaptive compared to the other approach in generating the gaits of the biped robot.
---
paper_title: Probabilistic Balance Monitoring for Bipedal Robots

paper_content:

In this paper, a probability-based balance monitoring concept for humanoid robots is proposed. Two algorithms are presented that allow us to distinguish between exceptional situations and normal operations. The first classification approach uses Gaussian-Mixture-Models (GMM) to describe the distribution of the robot's sensor data for typical situations such as stable walking or falling down. With the GMM it is possible to state the probability of the robot being in one of the known situations. The concept of the second algorithm is based on Hidden-Markov-Models (HMM). The objective is to detect and classify unstable situations by means of their typical sequences in the robot's sensor data. When appropriate reflex motions are linked to the critical situations, the robot can prevent most falls or is at least able to execute a controlled falling motion. The proposed algorithms are verified by simulations and experiments with our bipedal robot BARt-UH.
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paper_title: Automated evolutionary design, robustness, and adaptation of sidewinding locomotion of a simulated snake-like robot

paper_content:

Inspired by the efficient method of locomotion of the rattlesnake Crotalus cerastes, the objective of this work is automatic design through genetic programming (GP) of the fastest possible (sidewinding) locomotion of simulated limbless, wheelless snake-like robot (Snakebot). The realism of simulation is ensured by employing the Open Dynamics Engine (ODE), which facilitates implementation of all physical forces, resulting from the actuators, joints constrains, frictions, gravity, and collisions. Reduction of the search space of the GP is achieved by representation of Snakebot as a system comprising identical morphological segments and by automatic definition of code fragments, shared among (and expressing the correlation between) the evolved dynamics of the vertical and horizontal turning angles of the actuators of Snakebot. Empirically obtained results demonstrate the emergence of sidewinding locomotion from relatively simple motion patterns of morphological segments. Robustness of the sidewinding Snakebot, which is considered to be the ability to retain its velocity when situated in an unanticipated environment, is illustrated by the ease with which Snakebot overcomes various types of obstacles such as a pile of or burial under boxes, rugged terrain, and small walls. The ability of Snakebot to adapt to partial damage by gradually improving its velocity characteristics is discussed. Discovering compensatory locomotion traits, Snakebot recovers completely from single damage and recovers a major extent of its original velocity when more significant damage is inflicted. Exploring the opportunity for automatic design and adaptation of a simulated artifact, this work could be considered as a step toward building real Snakebots, which are able to perform robustly in difficult environments.
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paper_title: Autonomous evolution of dynamic gaits with two quadruped robots

paper_content:

A challenging task that must be accomplished for every legged robot is creating the walking and running behaviors needed for it to move. In this paper we describe our system for autonomously evolving dynamic gaits on two of Sony's quadruped robots. Our evolutionary algorithm runs on board the robot and uses the robot's sensors to compute the quality of a gait without assistance from the experimenter. First, we show the evolution of a pace and trot gait on the OPEN-R prototype robot. With the fastest gait, the robot moves at over 10 m/min, which is more than forty body-lengths/min. While these first gaits are somewhat sensitive to the robot and environment in which they are evolved, we then show the evolution of robust dynamic gaits, one of which is used on the ERS-110, the first consumer version of AIBO.
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paper_title: A movement pattern generator model using artificial neural networks

paper_content:

The authors have developed a movement pattern generator, using an artificial neural network (ANN) for generating periodic movement trajectories. This model is based on the concept of 'central pattern generators'. Jordan's (1986) sequential network, which is capable of learning sequences of patterns, was modified and used to generate several bipedal trajectories (or gaits), coded in task space, at different frequencies. The network model successfully learned all of the trajectories presented to it. The model has many attractive properties, such as limit cycle behavior, generalization of trajectories and frequencies, phase maintenance, and fault tolerance. The movement pattern generator model is potentially applicable for improved understanding of animal locomotion and for use in legged robots and rehabilitation medicine. >
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paper_title: A universal stability criterion of the foot contact of legged robots - adios ZMP

paper_content:

This paper proposes a universal stability criterion of the foot contact of legged robots. The proposed method checks if the sum of the gravity and the inertia wrench applied to the COG of the robot, which is proposed to be the stability criterion, is inside the polyhedral convex cone of the contact wrench between the feet of a robot and its environment. The criterion can be used to determine the strong stability of the foot contact when a robot walks on an arbitrary terrain and/or when the hands of the robot are in contact with it under the sufficient friction assumption. The determination is equivalent to check if the ZMP is inside the support polygon of the feet when the robot walks on a horizontal plane with sufficient friction. The criterion can also be used to determine if the foot contact is sufficiently weakly stable when the friction follows a physical law. Therefore, the proposed criterion can be used to judge what the ZMP can, and it can be used in more universal cases
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paper_title: A Learning Architecture Based on Reinforcement Learning for Adaptive Control of the Walking Machine LAURON

paper_content:

The learning of complex control behaviour of autonomous mobile robots is one of the actual research topics. In this article an intelligent control architecture is presented which integrates learning methods and available domain knowledge. This control architecture is based on Reinforcement Learning and allows continuous input and output parameters, hierarchical learning, multiple goals, self-organized topology of the used networks and online learning. As a testbed this architecture is applied to the six-legged walking machine LAURON to learn leg control and leg coordination.
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paper_title: Modeling of a Neural Pattern Generator with Coupled nonlinear Oscillators

paper_content:

A set of van der Pol oscillators is arranged in a network in which each oscillator is coupled to each other oscillator. Through the selection of coupling coefficients, the network is made to appear as a ring and as a chain of coupled oscillators. Each oscillator is provided with amplitude, frequency, and offset parameters which have analytically indeterminable effects on the output waves. These systems are simulated on the digital computer in order to study the amplitude, frequency, offset, and phase relationships of the waves versus parameter changes. Based on the simulations, systems of coupled oscillators are configured so that they exhibit stable patterns of signals which can be used to model the central pattern generator (CPG) of living organisms. Using a simple biped as an example locomotory system, the CPG model generates control signals for simulated walking and jumping maneuvers. It is shown that with parameter adjustments, as guided by the simulations, the model can be made to generate kinematic trajectories which closely resemble those for the human walking gait. Further-more, minor tuning of these parameters along with some algebraic sign changes of coupling coefficients can effect a transition in the trajectories to those of a two-legged hopping gait. The generalized CPG model is shown to be versatile enough that it can also generate various n-legged gaits and spinal undulatory motions, as in the swimming motions of a fish.
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paper_title: Soccer playing humanoid robots: Processing architecture, gait generation and vision system

paper_content:

Research on humanoid robotics in Mechatronics and Automation (MA) Laboratory, Electrical and Computer Engineering (ECE), National University of Singapore (NUS) was started at the beginning of this decade. Various research prototypes for humanoid robots have been designed and are going through evolution over these years. These humanoids have been successfully participating in various robotic soccer competitions. In this paper, three major research and development aspects of the above humanoid research are discussed. The paper focuses on various practical and theoretical considerations involved in processing architecture, gait generation and vision systems.
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paper_title: Design of a Central Pattern Generator Using Reservoir Computing for Learning Human Motion

paper_content:

To generate coordinated periodic movements, robot locomotion demands mechanisms which are able to learn and produce stable rhythmic motion in a controllable way. Because systems based on biological central pattern generators (CPGs) can cope with these demands, these kind of systems are gaining in success. In this work we introduce a novel methodology that uses the dynamics of a randomly connected recurrent neural network for the design of CPGs. When a randomly connected recurrent neural network is excited with one or more useful signals, an output can be trained by learning an instantaneous linear mapping of the neuron states. This technique is known as reservoir computing (RC). We will show that RC has the necessary capabilities to be fruitful in designing a CPG that is able to learn human motion which is applicable for imitation learning in humanoid robots.
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paper_title: Experiments in learning distributed control for a hexapod robot

paper_content:

Abstract This paper reports on experiments involving a hexapod robot. Motivated by neurobiological evidence that control in real hexapod insects is distributed leg-wise, we investigated two approaches to learning distributed controllers: genetic algorithms and reinforcement learning. In the case of reinforcement learning, a new learning algorithm was developed to encourage cooperation between legs. Results from both approaches are presented and compared.
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paper_title: Evolving Dynamical Neural Networks for Adaptive Behavior

paper_content:

We would like the behavior of the artificial agents that we construct to be as well-adapted to their environments as natural animals are to theirs. Unfortunately, designing controllers with these properties is a very difficult task. In this article, we demonstrate that continuous-time recurrent neural networks are a viable mechanism for adaptive agent control and that the genetic algorithm can be used to evolve effective neural controllers. A significant advantage of this approach is that one need specify only a measure of an agent's overall performance rather than the precise motor output trajectories by which it is achieved. By manipulating the performance evaluation, one can place selective pressure on the development of controllers with desired properties. Several novel controllers have been evolved, including a chemotaxis controller that switches between different strategies depending on environmental conditions, and a locomotion controller that takes advantage of sensory feedback if available but th...
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paper_title: Optimal Path and Gait Generations Simultaneously of a Six-legged Robot Using a GA-Fuzzy Approach

paper_content:

This paper describes a new method for generating optimal path and gait simultaneously of a six-legged robot using a combined GA-fuzzy approach. The problem of combined path and gait generations involves three steps, namely determination of vehicle’s trajectory, foothold selection and design of a sequence of leg movements. It is a complicated task and no single traditional approach is found to be successful in handling this problem. Moreover, the traditional approaches do not consider optimization issues, yet they are computationally expensive. Thus, the generated path and gaits may not be optimal in any sense. To solve such problems optimally, there is still a need for the development of an efficient and computationally faster algorithm. In the proposed genetic-fuzzy approach, optimal path and gaits are generated by using fuzzy logic controllers (FLCs) and genetic algorithms (GAs) are used to find optimized FLCs. The optimization is done off-line on a number of training scenarios and optimal FLCs are found. The hexapod can then use these GA-tuned FLCs to navigate in test-case scenarios.
---
paper_title: On-line stable gait generation of a two-legged robot using a genetic–fuzzy system

paper_content:

Abstract Gait generation for legged vehicles has since long been considered as an area of keen interest by the researchers. Soft computing is an emerging technique, whose utility is more stressed, when the problems are ill-defined, difficult to model and exhibit large scale solution spaces. Gait generation for legged vehicles is a complex task. Therefore, soft computing can be applied to solve it. In this work, gait generation problem of a two-legged robot is modeled using a fuzzy logic controller (FLC), whose rule base is optimized offline, using a genetic algorithm (GA). Two different GA-based approaches (to improve the performance of FLC) are developed and their performances are compared to that of manually constructed FLC. Once optimized, the FLCs will be able to generate dynamically stable gait of the biped. As the CPU-time of the algorithm is found to be only 0.002 s in a P-III PC, the algorithm is suitable for on-line (real-time) implementations.
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Title: Intelligent Approaches in Locomotion - A Review
Section 1: Introduction
Description 1: An overview of the paper and its objectives, highlighting the scope of the review and the method of categorization and comparison of techniques.

Section 2: Analytical approaches
Description 2: Discuss the oldest and frequently used method in locomotion control, including detailed explanation of biped and non-biped trajectories and optimization techniques.

Section 3: Central pattern generators and oscillators
Description 3: Explain the use of CPGs and oscillator models, their biological inspiration, and how they are implemented in robotic systems, including parameter selection and optimization.

Section 4: Neural networks
Description 4: Describe the use of conventional neural networks in locomotion, differentiating between feed-forward and recurrent neural networks, and their applications and training techniques.

Section 5: Hidden Markov Models
Description 5: Present the application of HMMs in robotic learning by imitation and their use in skill observation, classification, and imitation, including recognition and synthesis methods.

Section 6: Rule based systems
Description 6: Discuss systems that use transition tables and fuzzy logic for locomotion control, emphasizing their simplicity and ease of optimization, as well as advantages and disadvantages.

Section 7: Conclusion
Description 7: Summarize the comparison of various approaches, highlight the strengths and weaknesses of each method, and suggest criteria for selecting appropriate techniques based on application requirements.