Barkour：基于四足机器人的动物级敏捷性基准测试
Barkour: Benchmarking Animal-level Agility with Quadruped Robots
AI总结：
针对足式机器人缺乏敏捷性系统基准的问题，提出受狗敏捷赛启发的Barkour障碍赛道基准，并基于强化学习与Transformer蒸馏两种方法设定基线，使四足机器人能以狗一半的速度完成赛道，推动机器人向动物级敏捷性发展。
摘要：
动物进化出了多种敏捷的运动策略，如冲刺、跃步和跳跃。开发能像生物同类一样移动并展现各种敏捷技能以快速穿越复杂环境的足式机器人正受到越来越多的关注。尽管兴趣日益增长，但该领域缺乏系统性的基准来衡量控制策略和硬件在敏捷性方面的表现。我们引入了Barkour基准，这是一个用于量化足式机器人敏捷性的障碍赛道。受狗敏捷性比赛的启发，它由多样化的障碍物和基于时间的评分机制组成。这鼓励研究人员开发不仅能快速移动，而且能以可控且多功能的方式移动的控制器。为了设定强有力的基线，我们提出了两种方法来应对该基准。在第一种方法中，我们使用同策略强化学习方法训练专家运动技能，并将其与高级导航控制器相结合。在第二种方法中，我们将专家技能蒸馏为一个基于Transformer的通用运动策略，名为Locomotion-Transformer，该策略能够处理各种地形，并根据感知到的环境和机器人状态调整机器人的步态。使用定制的四足机器人，我们展示了我们的方法能以狗一半的速度完成赛道。我们希望我们的工作代表着朝着创建使机器人达到动物级敏捷性的控制器迈出了一步。
英文摘要：
Animals have evolved various agile locomotion strategies, such as sprinting, leaping, and jumping. There is a growing interest in developing legged robots that move like their biological counterparts and show various agile skills to navigate complex environments quickly. Despite the interest, the field lacks systematic benchmarks to measure the performance of control policies and hardware in agility. We introduce the Barkour benchmark, an obstacle course to quantify agility for legged robots. Inspired by dog agility competitions, it consists of diverse obstacles and a time based scoring mechanism. This encourages researchers to develop controllers that not only move fast, but do so in a controllable and versatile way. To set strong baselines, we present two methods for tackling the benchmark. In the first approach, we train specialist locomotion skills using on-policy reinforcement learning methods and combine them with a high-level navigation controller. In the second approach, we distill the specialist skills into a Transformer-based generalist locomotion policy, named Locomotion-Transformer, that can handle various terrains and adjust the robot's gait based on the perceived environment and robot states. Using a custom-built quadruped robot, we demonstrate that our method can complete the course at half the speed of a dog. We hope that our work represents a step towards creating controllers that enable robots to reach animal-level agility.
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。
