A Review of Past and Future Trends in Perceptual Anchoring.
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paper_title: To secure an anchor -- a recovery planning approach to ambiguity in perceptual anchoring

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An autonomous robot using symbolic reasoning, sensing and acting in a real environment needs the ability to create and maintain the connection between symbols representing objects in the world and the corresponding perceptual representations given by its sensors. This connection has been named perceptual anchoring. In complex environments, anchoring is not always easy to establish: the situation may often be ambiguous as to which percept actually corresponds to a given symbol. In this paper, we extend perceptual anchoring to deal robustly with ambiguous situations by providing general methods for detecting them and recovering from them. We consider different kinds of ambiguous situations. We also present methods to recover from these situations based on automatically formulating them as conditional planning problems that then are solved by a planner. We illustrate our approach by showing experiments involving a mobile robot equipped with a color camera and an electronic nose.
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paper_title: The Symbol Grounding Problem

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A black powder flamespread tester is disclosed in which powder to be qualified is packed in a sample tube provided with a plurality of sight holes. Light sensors are disposed adjacent each sight hole and emit signals upon flame front passage of the sight hole. The sample tube is removably secured to an ignition head which contains an electrically actuated pyrotechnic squib. The squib discharges gases into a transition chamber from which the gases pass through orifices in an orifice plate, through a void volume into the black powder bed, which ignites the black powder. The ignition head is removably secured to a tester chamber in which the sample holder is received. Light sensors are housed in a separate fin which is removably received in an aperture in the tester chamber wall. After the squib gas ignites the powder bed, the output signal of each light sensor increases to saturation as the flame front passes its respective sight hole. The output signal from each light sensor is conditioned and summed to form one output signal. A plot of the summed output signal versus time is prepared, which shows the time of passage of the flame front through the black powder from each light sensor to the next. The flamespread rate is determined by dividing the known distance between light sensors by the time of passage of the flame front between respective light sensors.
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paper_title: Anchoring Symbols to Sensor Data: Preliminary Report

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Anchoring is the process of creating and maintaining the correspondence between symbols and percepts that refer to the same physical objects. Although this pro- cess must necessarily be present in any physically em- bedded system that includes a symbolic component (e.g., an autonomous robot), no systematic study of anchoring as a problem per se has been reported in the literature on intelligent systems. In this paper, we propose a domain-independent deflnition of the anchor- ing problem, and identify its three basic functionalities: flnd, reacquire, and track. We illustrate our deflnition on two systems operating in two difierent domains: an unmanned airborne vehicle for tra-c surveillance; and a mobile robot for o-ce navigation.
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paper_title: Anchoring Symbols to Sensor Data: Preliminary Report

paper_content:

Anchoring is the process of creating and maintaining the correspondence between symbols and percepts that refer to the same physical objects. Although this pro- cess must necessarily be present in any physically em- bedded system that includes a symbolic component (e.g., an autonomous robot), no systematic study of anchoring as a problem per se has been reported in the literature on intelligent systems. In this paper, we propose a domain-independent deflnition of the anchor- ing problem, and identify its three basic functionalities: flnd, reacquire, and track. We illustrate our deflnition on two systems operating in two difierent domains: an unmanned airborne vehicle for tra-c surveillance; and a mobile robot for o-ce navigation.
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paper_title: Maintaining Coherent Perceptual Information Using Anchoring

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The purpose of this paper is to address the problem of maintaining coherent perceptual information in a mobile robotic system working over extended periods of time, interacting with a user and using multiple sensing modalities to gather information about the environment and specific objects. We present a system which is able to use spatial and olfactory sensors to patrol a corridor and execute user requested tasks. To cope with perceptual maintenance we present an extension of the anchoring framework capable of maintaining the correspondence between sensor data and the symbolic descriptions referring to objects. It is also capable of tracking and acquiring information from observations derived from sensor-data as well as information from a priori symbolic concepts. The general system is described and an experimental validation on a mobile robot is presented.
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paper_title: Conceptual Spaces: The Geometry of Thought

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Within cognitive science, two approaches currently dominate the problem of modeling representations. The symbolic approach views cognition as computation involving symbolic manipulation. Connectionism, a special case of associationism, models associations using artificial neuron networks. Peter Gardenfors offers his theory of conceptual representations as a bridge between the symbolic and connectionist approaches. Symbolic representation is particularly weak at modeling concept learning, which is paramount for understanding many cognitive phenomena. Concept learning is closely tied to the notion of similarity, which is also poorly served by the symbolic approach. Gardenfors's theory of conceptual spaces presents a framework for representing information on the conceptual level. A conceptual space is built up from geometrical structures based on a number of quality dimensions. The main applications of the theory are on the constructive side of cognitive science: as a constructive model the theory can be applied to the development of artificial systems capable of solving cognitive tasks. Gardenfors also shows how conceptual spaces can serve as an explanatory framework for a number of empirical theories, in particular those concerning concept formation, induction, and semantics. His aim is to present a coherent research program that can be used as a basis for more detailed investigations. (Less)
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paper_title: Symbol grounding through robotic manipulation in cognitive systems

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Though proposals have been put forth to solve the classical symbol grounding problem through robotic sensorimotor interactions, only little progress has been made in this direction with actual working systems, and symbol grounding through physical interaction has been rarely dealt with. We address this problem in the context of robotic manipulation for cognitive systems, and claim that there are symbols which do not refer simply to physical objects but rather to the embodied interactions between the robot and the objects in its environment. Through the description of two manipulation experiments we offer a proposal on which to build a theory of symbolic representations for physical interactions. Some important neuroscience studies that support our view are also briefly described.
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paper_title: Anchoring: do we need new solutions to an old problem or do we have old solutions for a new problem?

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We propose a model to represent knowledge in an agent possibly participating to a multi-agent system, such that the anchoring problem can be faced by well known techniques. We discuss how this model could reliably support the instantiation of concepts by aggregating percepts affected by uncertainty, sensed by several sensors, and obtained from different agents.
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paper_title: Identifying an Object that is Perceptually Indistinguishable from One Previously Perceived

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People often encounter objects that are perceptually indistinguishable from objects that they have seen before. When this happens, how do they decide whether the object they are looking at is something never before seen, or if it is the same one they encountered before? To identify these objects people surely use background knowledge and contextual cues. We propose a computational theory of identifying perceptually indistinguishable objects(PIOs) based on a set of experiments which were designed to identify the knowledge and perceptual cues that people use to identify PIOs. By identifying a PIO, we mean deciding which individual object is encountered, not deciding what category of objects it belongs to. In particular, identifying a PIO means deciding if the object just encountered is a new, never before seen object, or if it has been previously encountered, which previously perceived object it is. Our agent’s beliefs and reasoning are based on an intensional representation (Maida & Shapiro 1982). Intensional representations model the sense (Frege 1892) of an object rather than the object referent, itself. The terms of our representation language, SNePS (Shapiro & the SNePS Implementation Group 2002; Shapiro & Rapaport 1992), denote mental entities. Some such entities are propositions; others are abstract ideas; others are the agent’s “concepts” or “ideas” of objects in the world. This is important for the task of identifying PIOs, because before the identification task is complete, the agent may have two mental entities, e1 and e2, that it might or might not conclude correspond to the same object in the world.
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paper_title: Autonomous development of a grounded object ontology by a learning robot

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We describe how a physical robot can learn about objects from its own autonomous experience in the continuous world. The robot identifies statistical regularities that allow it to represent a physical object with a cluster of sensations that violate a static world model, track that cluster over time, extract percepts from that cluster, form concepts from similar percepts, and learn reliable actions that can be applied to objects. We present a formalism for representing the ontology for objects and actions, a learning algorithm, and the results of an evaluation with a physical robot.
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paper_title: Carl: from situated activity to language level interaction and learning

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Carl is a prototype of an intelligent service robot, designed having in mind for such tasks as serving food in a reception or acting as a host in an organization. The approach that has been followed in the design of Carl is based on an explicit concern with the integration of the major dimensions of intelligence, namely communication, action, reasoning and learning. Although different communities have thoroughly studied these dimensions in the past, their integration has seldom been attempted in a systematic way. This paper describes the software architecture of Carl as well as the main modules, from sensor fusion and navigation to language-level communication and learning.
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paper_title: Enhancing the Awareness of Decentralized Cooperative Mobile Robots through Active Perceptual Anchoring

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In this paper, we describe a system for controlling the perceptual processes of two cooperative mobile robots that addresses the issue of enhancing perceptual awareness. We define awareness here as knowing the location of other robots in the environment. The proposed system benefits from a formalism called perceptual anchoring. Here, perceptual anchoring enhances the awareness of the system by employing an anchor-based active gaze control strategy or active perceptual anchoring to control the perceptual effort according to what is important at a given time. By anchoring we extend the notion of awareness as knowing what the symbols in the control module represent to by connecting them to the objects or features in the environment. We demonstrate the present system through a simulation of two nonholonomic mobile robots performing a cooperative transportation by carrying a cargo to a target location where there are two other robots moving about. The system is able to efficiently focus the perceptual effort and thus able to safely carry the cargo to the target position.
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paper_title: Learning to communicate in a decentralized environment

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Learning to communicate is an emerging challenge in AI research. It is known that agents interacting in decentralized, stochastic environments can benefit from exchanging information. Multi-agent planning generally assumes that agents share a common means of communication; however, in building robust distributed systems it is important to address potential miscoordination resulting from misinterpretation of messages exchanged. This paper lays foundations for studying this problem, examining its properties analytically and empirically in a decision-theoretic context. We establish a formal framework for the problem, and identify a collection of necessary and sufficient properties for decision problems that allow agents to employ probabilistic updating schemes in order to learn how to interpret what others are communicating. Solving the problem optimally is often intractable, but our approach enables agents using different languages to converge upon coordination over time. Our experimental work establishes how these methods perform when applied to problems of varying complexity.
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paper_title: Semiotic Schemas: A Framework for Grounding Language in Action and Perception

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A theoretical framework for grounding language is introduced that provides a computational path from sensing and motor action to words and speech acts. The approach combines concepts from semiotics and schema theory to develop a holistic approach to linguistic meaning. Schemas serve as structured beliefs that are grounded in an agent's physical environment through a causal-predictive cycle of action and perception. Words and basic speech acts are interpreted in terms of grounded schemas. The framework reflects lessons learned from implementations of several language processing robots. It provides a basis for the analysis and design of situated, multimodal communication systems that straddle symbolic and non-symbolic realms.
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paper_title: Modelling Spatio-Temporal Comprehension in Situated Human-Robot Dialogue as Reasoning about Intentions and Plans.

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The article presents an approach to modeling spatio-temporal comprehension in situated dialogue. The approach combines linguistic reasoning with reasoning about intentions and plans during incremental interpretation of dialogue moves. The article explores how intention-directed planning can prime selectional attention in utterance comprehension by disambiguating linguistic analyses on the basis of plan availability, and by raising expectations what action(s) may be mentioned next. Also, planning can complement linguistic analyses with details on spatiotemporal-causal structure established in planning inferences. This makes such inferences available for future referencing in the discourse context.
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paper_title: On the Integration of Grounding Language and Learning Objects

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This paper presents a multimodal learning system that can ground spoken names of objects in their physical referents and learn to recognize those objects simultaneously from naturally co-occurring multisensory input. There are two technical problems involved: (1) the correspondence problem in symbol grounding - how to associate words (symbols) with their perceptually grounded meanings from multiple cooccurrences between words and objects in the physical environment. (2) object learning - how to recognize and categorize visual objects. We argue that those two problems can be fundamentally simplified by considering them in a general system and incorporating the spatio-temporal and cross-modal constraints of multimodal data. The system collects egocentric data including image sequences as well as speech while users perform natural tasks. It is able to automatically infer the meanings of object names from vision, and categorize objects based on teaching signals potentially encoded in speech. The experimental results reported in this paper reveal the effectiveness of using multimodal data and integrating heterogeneous techniques in machine learning, natural language processing and computer vision.
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paper_title: Carl: from situated activity to language level interaction and learning

paper_content:

Carl is a prototype of an intelligent service robot, designed having in mind for such tasks as serving food in a reception or acting as a host in an organization. The approach that has been followed in the design of Carl is based on an explicit concern with the integration of the major dimensions of intelligence, namely communication, action, reasoning and learning. Although different communities have thoroughly studied these dimensions in the past, their integration has seldom been attempted in a systematic way. This paper describes the software architecture of Carl as well as the main modules, from sensor fusion and navigation to language-level communication and learning.
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paper_title: A Distributed Architecture for Symbolic Data Fusion

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This paper presents a distributed knowledge representation and data fusion system designed for highly integrated Ambient Intelligence applications. The architecture, based on the idea of an ecosystem of interacting artificial entities, is a framework for collaborating agents to perform an intelligent multi-sensor data fusion. In particular, we focus on the cognitive layers leading the overall data acquisition process. The approach has been thoroughly tested in simulation, and part of it has been already exploited in successful applications.
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paper_title: Symbiotic Robotic Systems: Humans, Robots, and Smart Environments

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In general symbiotic robotic systems can be considered three-agent systems in which the agents are performing interdependent tasks. In such systems the robot's actions are generally controllable and observable the environment's actions are partially controllable and partially observable, and the human's actions are not (directly) controllable but are partially observable. This new paradigm will enable us to incorporate techniques from AI and robotics in everyday, smart environments. Symbiotic robotic systems will shift our focus from the development of fully autonomous, almighty robot servants to the development of an open, extensible environment in which humans will cohabitate with many cooperating robotic devices
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Title: A Review of Past and Future Trends in Perceptual Anchoring
Section 1: Introduction
Description 1: Introduction to the problem of anchoring in robotic systems and the importance of connecting symbolic and signal-level representations.

Section 2: An example of an anchoring framework
Description 2: Presentation of a specific anchoring framework and discussion of its core functionalities in a robotic system.

Section 3: Creation of anchors
Description 3: Explanation of the methods for creating anchors both in a top-down and bottom-up fashion.

Section 4: Maintenance of anchors
Description 4: Description of the functions required to track and maintain anchors over time and the challenges involved.

Section 5: Deletion of anchors
Description 5: Discussion of the mechanisms for removing invalid anchors and dealing with challenges associated with this process.

Section 6: Integration of the functionalities
Description 6: Overview of how the different anchoring functionalities are integrated within the framework.

Section 7: Case study
Description 7: Illustration of how the anchoring module operates within a corridor monitoring scenario.

Section 8: Other approaches to anchoring
Description 8: Review of alternative methods and frameworks for addressing the anchoring problem.

Section 9: Cooperative anchoring
Description 9: Examination of the anchoring problem within multi-robot systems and the complexities involved.

Section 10: Anchoring for human robot interaction
Description 10: Exploration of the significance of anchoring in human-robot interaction and relevant applications.

Section 11: Anchoring in symbiotic robotic systems
Description 11: Discussion of anchoring within symbiotic robotic systems and the additional challenges posed.

Section 12: Conclusions
Description 12: Summary of the key points discussed and the importance of symbol-percept correspondence in robotic systems.