二足歩行ロボットの堅牢なパラメータ化歩行制御のための強化学習
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots
シミュレーションでドメインランダム化を用いたモデルフリー強化学習により歩行方策を学習し、実機Cassieに転送して速度・高さ・旋回の多様な歩行を従来手法より堅牢に実現した。
著者: Zhongyu Li, Xuxin Cheng, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath
分類: cs.RO, cs.AI, cs.LG, cs.SY, eess.SY
原文アブストラクト
Developing robust walking controllers for bipedal robots is a challenging endeavor. Traditional model-based locomotion controllers require simplifying assumptions and careful modelling; any small errors can result in unstable control. To address these challenges for bipedal locomotion, we present a model-free reinforcement learning framework for training robust locomotion policies in simulation, which can then be transferred to a real bipedal Cassie robot. To facilitate sim-to-real transfer, domain randomization is used to encourage the policies to learn behaviors that are robust across variations in system dynamics. The learned policies enable Cassie to perform a set of diverse and dynamic behaviors, while also being more robust than traditional controllers and prior learning-based methods that use residual control. We demonstrate this on versatile walking behaviors such as tracking a target walking velocity, walking height, and turning yaw.
