最小限の人的労力で実世界を歩くことを学ぶ
Learning to Walk in the Real World with Minimal Human Effort
実機ロボット上で深層強化学習により歩行方策を学習するシステムを開発し、自動データ収集と安全制約付きRLで3種類の地形での歩行獲得を実現した。
著者: Sehoon Ha, Peng Xu, Zhenyu Tan, Sergey Levine, Jie Tan
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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. The supplemental video can be found at: \url{https://youtu.be/cwyiq6dCgOc}.
