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arXiv · 1910.02556

Bio-inspired Learning of Sensorimotor Control for Locomotion

Tixian Wang·Amirhossein Taghvaei·Prashant G. Mehta
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Abstract

This paper presents a bio-inspired central pattern generator (CPG)-type architecture for learning optimal maneuvering control of periodic locomotory gaits. The architecture is presented here with the aid of a snake robot model problem involving planar locomotion of coupled rigid body systems. The maneuver involves clockwise or counterclockwise turning from a nominally straight path. The CPG circuit is realized as a coupled oscillator feedback particle filter. The collective dynamics of the filter are used to approximate a posterior distribution that is used to construct the optimal control input for maneuvering the robot. A Q-learning algorithm is applied to learn the approximate optimal control law. The issues surrounding the parametrization of the Q-function are discussed. The theoretical results are illustrated with numerics for a 5-link snake robot system.

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Tixian Wang, Amirhossein Taghvaei, Prashant G. Mehta. 2019-10-03. Bio-inspired Learning of Sensorimotor Control for Locomotion. https://arxiv.org/abs/1910.02556

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