Learning to Walk in the Real World with Minimal Human Effort

Authors: Sehoon Ha, Peng Xu, Zhenyu Tan, Sergey Levine, Jie Tan Paper: 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