Bio-inspired Learning of Sensorimotor Control for Locomotion
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.
Keywords
Cite
@article{arxiv.1910.02556,
title = {Bio-inspired Learning of Sensorimotor Control for Locomotion},
author = {Tixian Wang and Amirhossein Taghvaei and Prashant G. Mehta},
journal= {arXiv preprint arXiv:1910.02556},
year = {2019}
}
Comments
8 pages, 8 figures, submitted to The 2020 American Control Conference. arXiv admin note: text overlap with arXiv:1910.00107