机器人Parkour学习
Robot Parkour Learning
AI总结：
本研究提出一种无需参考运动数据的端到端基于视觉的强化学习系统，通过直接配点法生成并蒸馏多种parkour技能至单一策略，使低成本四足机器人能自主选择并执行技能以穿越复杂真实环境。
摘要：
Parkour是腿式运动的一项重大挑战，要求机器人在复杂环境中快速克服各种障碍。现有方法通过使用参考动物数据或复杂的奖励函数，只能生成多样但盲目的运动技能，或是基于视觉但高度专用的技能。然而，自主parkour要求机器人学习既基于视觉又具备多样性的可泛化技能，以感知并应对各种场景。在本研究中，我们提出了一种系统，使用简单的奖励且无需任何参考运动数据，学习包含多种parkour技能的单一端到端基于视觉的parkour策略。我们开发了一种受直接配点法启发的强化学习方法来生成parkour技能，包括攀爬高障碍物、跨越宽缝隙、在低矮障碍下爬行、挤过狭窄缝隙以及奔跑。我们将这些技能蒸馏为单一的基于视觉的parkour策略，并利用其自我中心深度相机将其部署到四足机器人上。我们证明，我们的系统能够赋能两种不同的低成本机器人，使其自主选择并执行适当的parkour技能，从而穿越具有挑战性的真实世界环境。
英文摘要：
Parkour is a grand challenge for legged locomotion that requires robots to overcome various obstacles rapidly in complex environments. Existing methods can generate either diverse but blind locomotion skills or vision-based but specialized skills by using reference animal data or complex rewards. However, autonomous parkour requires robots to learn generalizable skills that are both vision-based and diverse to perceive and react to various scenarios. In this work, we propose a system for learning a single end-to-end vision-based parkour policy of diverse parkour skills using a simple reward without any reference motion data. We develop a reinforcement learning method inspired by direct collocation to generate parkour skills, including climbing over high obstacles, leaping over large gaps, crawling beneath low barriers, squeezing through thin slits, and running. We distill these skills into a single vision-based parkour policy and transfer it to a quadrupedal robot using its egocentric depth camera. We demonstrate that our system can empower two different low-cost robots to autonomously select and execute appropriate parkour skills to traverse challenging real-world environments.
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。
