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Learning to Walk in the Real World with Minimal Human Effort

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Sehoon Ha (Georgia Institute of Technology); Peng Xu (Google Inc); Zhenyu Tan (Google); Sergey Levine (UC Berkeley)*; Jie Tan (Google)

Interactive Session

2020-11-17, 11:50 - 12:20 PST | PheedLoop Session


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.


Reviews and Rebuttal

Reviews & Rebuttal

Conference on Robot Learning 2020