  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.
