“Learning to Walk in the Real World With Minimal Human Effort”, 2020-02-20 (; similar):
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 3 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: https://www.youtube.com/watch?v=cwyiq6dCgOc.