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

Learning to Walk in the Real World with Minimal Human Effort