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.

翻译：动物已进化出多种敏捷运动策略，如冲刺、腾跃和跳跃。开发能够像生物对应物一样运动、展现多种敏捷技能以在复杂环境中快速穿梭的腿式机器人正日益引起关注。尽管兴趣浓厚，但该领域仍缺乏系统性的基准测试来评估控制策略与硬件在敏捷性方面的性能。我们引入Barkour基准测试——一个用于量化腿式机器人敏捷性的障碍赛道。受犬类敏捷性竞赛启发，该赛道包含多样化的障碍物和基于时间的评分机制，鼓励研究者开发不仅移动迅速、且具有可控性与多能性的控制器。为建立稳健基线，我们提出两种应对该基准的方法。第一种方法采用在线强化学习方法训练专项运动技能，并结合高层导航控制器。第二种方法将专项技能蒸馏为基于Transformer的通用运动策略（名为Locomotion-Transformer），该策略可根据感知环境与机器人状态处理多种地形并调整步态。通过自研四足机器人，我们验证该方法能以犬类一半的速度完成赛道。期望这项工作能成为推动机器人达到动物级敏捷性的控制器研发的重要一步。