TY - GEN
T1 - Bio-inspired Learning of Sensorimotor Control for Locomotion
AU - Wang, Tixian
AU - Taghvaei, Amirhossein
AU - Mehta, Prashant G.
N1 - Publisher Copyright:
© 2020 AACC.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85089578563&partnerID=8YFLogxK
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U2 - 10.23919/ACC45564.2020.9147889
DO - 10.23919/ACC45564.2020.9147889
M3 - Conference contribution
AN - SCOPUS:85089578563
T3 - Proceedings of the American Control Conference
SP - 2188
EP - 2193
BT - 2020 American Control Conference, ACC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 American Control Conference, ACC 2020
Y2 - 1 July 2020 through 3 July 2020
ER -