Learning Free Gait Transition for Quadruped Robots Via Phase-Guided Controller

Gaits and transitions are key components in legged locomotion. For legged robots, describing and reproducing gaits as well as transitions remain longstanding challenges. Reinforcement learning has become a powerful tool to formulate controllers for legged robots. Learning multiple gaits and transiti...

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Vydané v:IEEE robotics and automation letters Ročník 7; číslo 2; s. 1230 - 1237
Hlavní autori: Shao, Yecheng, Jin, Yongbin, Liu, Xianwei, He, Weiyan, Wang, Hongtao, Yang, Wei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Foot
Gait
Learning
Legged locomotion
legged robots
Locomotion
machine learning for robot control
Oscillators
Phases
Quadrupedal robots
Reinforcement learning
Robot dynamics
Robots
Task analysis
Training
ISSN:2377-3766, 2377-3766
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Shrnutí:Gaits and transitions are key components in legged locomotion. For legged robots, describing and reproducing gaits as well as transitions remain longstanding challenges. Reinforcement learning has become a powerful tool to formulate controllers for legged robots. Learning multiple gaits and transitions, nevertheless, is related to the multi-task learning problems. In this work, we present a novel framework for training a simple control policy for a quadruped robot to locomote in various gaits. Four independent phases are used as the interface between the gait generator and the control policy, which characterizes the movement of four feet. Guided by the phases, the quadruped robot is able to locomote according to the generated gaits, such as walk, trot, pacing and bounding, and to make transitions among those gaits. More general phases can be used to generate complex gaits, such as mixed rhythmic dancing. With the control policy, the Black Panther robot, a medium-dog-sized quadruped robot, can perform all learned motor skills while following the velocity commands smoothly and robustly in natural environment.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3136645