学习在现实世界中行走，最小的人力
Learning to Walk in the Real World with Minimal Human Effort
摘要：
可靠稳定的运动一直是足式机器人面临的最基本的挑战之一。深度强化学习（deep RL）已经成为自主开发此类控制策略的一种有前途的方法。在本文中，我们开发了一个系统，用于在现实世界中以最少的人力来学习深度RL的腿部运动策略。机器人学习系统的关键难点是数据汇集的自动化和安全性.我们克服了这两个挑战，通过开发一个多任务的学习过程和安全约束的RL框架。我们测试了我们的系统在三种不同地形上学习行走的任务：平坦的地面，柔软的床垫和有裂缝的门垫。我们的系统可以自动和有效地学习Minitaur机器人的运动技能，几乎没有人为干预。补充视频可在以下网址找到：\url{https://youtu.be/cwyiq6dCgOc}。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
