论文标题
学习以最少的人类努力在现实世界中行走
Learning to Walk in the Real World with Minimal Human Effort
论文作者
论文摘要
可靠，稳定的运动一直是腿部机器人最根本的挑战之一。深度强化学习（DEEP RL）已成为一种自主制定此类控制政策的有前途的方法。在本文中，我们开发了一个系统，用于以最少的人为努力在现实世界中以深度RL学习腿部的运动政策。机器人学习系统的主要困难是自动数据收集和安全性。我们通过制定多任务学习程序和安全受限的RL框架来克服这两个挑战。我们测试了学习在三个不同地形上行走的任务：平坦的地面，柔软的床垫和带缝隙的门垫。我们的系统可以自动有效地学习微型机器人的运动技能，而没有人为干预。补充视频可以在：\ url {https://youtu.be/cwyiq6dcgoc}中找到。
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}.
