@inproceedings{52ba51dbf5cd4a28871bdbbb3d778f62,
title = "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.",
author = "Tixian Wang and Amirhossein Taghvaei and Mehta, \{Prashant G.\}",
note = "Financial support from the ONR MURI grant N00014-19-1-2373 and the ARO grant W911NF1810334 is gratefully acknowledged.; 2020 American Control Conference, ACC 2020 ; Conference date: 01-07-2020 Through 03-07-2020",
year = "2020",
month = jul,
doi = "10.23919/ACC45564.2020.9147889",
language = "English (US)",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2188--2193",
booktitle = "2020 American Control Conference, ACC 2020",
address = "United States",
}