In this paper, we present a general learning framework for controlling a quadruped robot that can mimic the behavior of real animals and traverse challenging terrains. Our method consists of two steps: an imitation learning step to learn from motions of real animals, and a terrain adaptation step to enable generalization to unseen terrains. We capture motions from a Labrador on various terrains to facilitate terrain adaptive locomotion. Our experiments demonstrate that our policy can traverse various terrains and produce a natural-looking behavior. We deployed our method on the real quadruped robot Max via zero-shot simulation-to-reality transfer, achieving a speed of 1.1 m/s on stairs climbing.

翻译：本文提出了一种通用学习框架，用于控制四足机器人模仿真实动物的行为并穿越复杂地形。该方法包含两个步骤：通过模仿学习从真实动物运动中学习，以及通过地形自适应步骤实现对未知地形的泛化。我们采集了拉布拉多犬在不同地形上的运动数据以促进地形自适应运动。实验表明，我们的策略能够穿越多种地形并产生自然逼真的行为。通过零样本仿真到现实迁移，我们在真实四足机器人Max上部署该方法，在攀爬楼梯时实现了1.1米/秒的速度。