强化学习
机器人运动
机器人
计算机科学
两足动物
鲁棒控制
集合(抽象数据类型)
人工智能
控制(管理)
参数化复杂度
控制工程
控制理论(社会学)
作者
Zhongyu Li,Xuxin Cheng,Xue Bin Peng,Pieter Abbeel,Sergey Levine,Glen Berseth,Koushil Sreenath
出处
期刊:arXiv: Robotics
日期:2021-03-26
被引量:1
标识
DOI:10.48550/arxiv.2103.14295
摘要
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.
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