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arXiv · 2002.08550

Learning to Walk in the Real World with Minimal Human Effort

Sehoon Ha·Peng Xu·Zhenyu Tan·Sergey Levine·Jie Tan
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Abstract

Reliable and stable locomotion has been one of the most fundamental challenges for legged robots. Deep reinforcement learning (deep RL) has emerged as a promising method for developing such control policies autonomously. In this paper, we develop a system for learning legged locomotion policies with deep RL in the real world with minimal human effort. The key difficulties for on-robot learning systems are automatic data collection and safety. We overcome these two challenges by developing a multi-task learning procedure and a safety-constrained RL framework. We tested our system on the task of learning to walk on three different terrains: flat ground, a soft mattress, and a doormat with crevices. Our system can automatically and efficiently learn locomotion skills on a Minitaur robot with little human intervention. The supplemental video can be found at: \url{https://youtu.be/cwyiq6dCgOc}.

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BibTeXRIS

Sehoon Ha, Peng Xu, Zhenyu Tan, Sergey Levine, Jie Tan. 2020-02-20. Learning to Walk in the Real World with Minimal Human Effort. https://arxiv.org/abs/2002.08550

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