Reinforcement Learning With Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion
نویسندگان
چکیده
Recently reinforcement learning (RL) has emerged as a promising approach for quadrupedal locomotion, which can save the manual effort in conventional approaches such designing skill-specific controllers. However, due to complex nonlinear dynamics robots and reward sparsity, it is still difficult RL learn effective gaits from scratch, especially challenging tasks walking over balance beam. To alleviate difficulty, we propose novel RL-based that contains an evolutionary foot trajectory generator. Unlike prior methods use fixed generator, generator continually optimizes shape of output given task, providing diversified motion priors guide policy learning. The trained with residual control signals fit different gaits. We then optimize network alternatively stabilize training share exploratory data improve sample efficiency. As result, our solve range simulation by including on beam crawling through cave. further verify effectiveness approach, deploy controller learned 12-DoF robot, successfully traverse scenarios efficient provide video show YouTube. 1 1 [Online]. Available: youtube.com/watch?v=hgBLR09MEOw, code available Github: github.com/PaddlePaddle/PaddleRobotics
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ژورنال
عنوان ژورنال: IEEE robotics and automation letters
سال: 2022
ISSN: ['2377-3766']
DOI: https://doi.org/10.1109/lra.2022.3145495