Learning to Walk in the Real World with Minimal Human Effort. (arXiv:2002.08550v1 [cs.RO])

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, an
automatic reset controller, 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.

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