Quadrupedal robots have played a crucial role in various environments, from structured environments to complex harsh terrains, thanks to their agile locomotion ability. However, these robots can easily lose their locomotion functionality if damaged by external accidents or internal malfunctions. In this paper, we propose a novel deep reinforcement learning framework to enable a quadrupedal robot to walk with impaired joints. The proposed framework consists of three components: 1) a random joint masking strategy for simulating impaired joint scenarios, 2) a joint state estimator to predict an implicit status of current joint condition based on past observation history, and 3) progressive curriculum learning to allow a single network to conduct both normal gait and various joint-impaired gaits. We verify that our framework enables the Unitree's Go1 robot to walk under various impaired joint conditions in real-world indoor and outdoor environments.

翻译：四足机器人凭借其敏捷的运动能力，在从结构化环境到复杂崎岖地形的各类场景中发挥了关键作用。然而，这些机器人若因外部事故或内部故障而受损，其运动功能极易丧失。本文提出一种新颖的深度强化学习框架，使四足机器人能够在关节受损条件下行走。该框架由三个组件构成：1）随机关节掩码策略，用于模拟关节受损情景；2）关节状态估计器，基于历史观测数据预测当前关节状态的隐式表征；3）渐进式课程学习，使单一网络能够同时执行正常步态及各类关节受损步态。我们通过实验验证，该框架使宇树科技Go1机器人在真实室内外环境中的多种关节受损条件下仍能保持行走能力。