Hierarchical generative modelling for autonomous robots
Kai Yuan Noor Sajid Karl Friston Zhibin Li
Humans can produce complex whole-body motions when interacting with their
surroundings, by planning, executing and combining individual limb movements.
We investigated this fundamental aspect of motor control in the setting of
autonomous robotic operations. We approach this problem by hierarchical
generative modelling equipped with multi-level planning-for autonomous task
completion-that mimics the deep temporal architecture of human motor control.
Here, temporal depth refers to the nested time scales at which successive
levels of a forward or generative model unfold, for example, delivering an
object requires a global plan to contextualise the fast coordination of
multiple local movements of limbs. This separation of temporal scales also
motivates robotics and control. Specifically, to achieve versatile sensorimotor
control, it is advantageous to hierarchically structure the planning and
low-level motor control of individual limbs. We use numerical and physical
simulation to conduct experiments and to establish the efficacy of this
formulation. Using a hierarchical generative model, we show how a humanoid
robot can autonomously complete a complex task that necessitates a holistic use
of locomotion, manipulation, and grasping. Specifically, we demonstrate the
ability of a humanoid robot that can retrieve and transport a box, open and
walk through a door to reach the destination, approach and kick a football,
while showing robust performance in presence of body damage and ground
irregularities. Our findings demonstrated the effectiveness of using
human-inspired motor control algorithms, and our method provides a viable
hierarchical architecture for the autonomous completion of challenging
goal-directed tasks.