Computer Science > Robotics
This paper has been withdrawn by Chuanyu Yang
[Submitted on 20 May 2020 (v1), last revised 11 Feb 2021 (this version, v2)]
Title:Learning natural locomotion behaviors for humanoid robots using human knowledge
No PDF available, click to view other formatsAbstract:This paper presents a new learning framework that leverages the knowledge from imitation learning, deep reinforcement learning, and control theories to achieve human-style locomotion that is natural, dynamic, and robust for humanoids. We proposed novel approaches to introduce human bias, i.e. motion capture data and a special Multi-Expert network structure. We used the Multi-Expert network structure to smoothly blend behavioral features, and used the augmented reward design for the task and imitation rewards. Our reward design is composable, tunable, and explainable by using fundamental concepts from conventional humanoid control. We rigorously validated and benchmarked the learning framework which consistently produced robust locomotion behaviors in various test scenarios. Further, we demonstrated the capability of learning robust and versatile policies in the presence of disturbances, such as terrain irregularities and external pushes.
Submission history
From: Chuanyu Yang [view email][v1] Wed, 20 May 2020 17:01:26 UTC (2,028 KB)
[v2] Thu, 11 Feb 2021 15:16:44 UTC (1 KB) (withdrawn)
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