Abstract
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.
Abstract (translated)
动物已经发展了许多敏捷的运动策略，例如跑步、跳跃和跳跃。对开发像生物学中动物一样的腿机器人越来越感兴趣，并且它们表现出各种敏捷技能，以快速适应复杂的环境。尽管有兴趣，该领域缺乏 systematic 基准来测量控制政策和硬件在敏捷性的表现的。我们介绍了Barkour 基准，这是一个用于衡量腿机器人敏捷性的障碍 Course。受到狗敏捷性竞赛的启发，它由多种障碍物和一个时间评分机制组成。这鼓励研究人员开发不仅可以快速移动，而且可以在可控和多功能性方面进行控制的控制器。为了建立强有力的基准，我们提出了两种方法来处理基准。在第一种方法中，我们使用基于政策强化学习的方法来训练专门的腿动技能，并将其与高级导航控制器相结合。在第二种方法中，我们将该专业技能分解成一个基于Transformer的通用腿动策略，名为 Locomotion-Transformer，它可以处理各种地形并根据感知的环境和机器人状态调整机器人的步伐。使用定制的四足机器人，我们演示了我们的方法可以在狗的速度快一半的情况下完成课程。我们希望我们的工作代表了创建一个控制器，使机器人能够达到动物级别的敏捷性的步骤。
URL
https://arxiv.org/abs/2305.14654
