Quadruped robots have strong adaptability to extreme environments but may also experience faults. Once these faults occur, robots must be repaired before returning to the task, reducing their practical feasibility. One prevalent concern among these faults is actuator degradation, stemming from factors like device aging or unexpected operational events. Traditionally, addressing this problem has relied heavily on intricate fault-tolerant design, which demands deep domain expertise from developers and lacks generalizability. Learning-based approaches offer effective ways to mitigate these limitations, but a research gap exists in effectively deploying such methods on real-world quadruped robots. This paper introduces a pioneering teacher-student framework rooted in reinforcement learning, named Actuator Degradation Adaptation Transformer (ADAPT), aimed at addressing this research gap. This framework produces a unified control strategy, enabling the robot to sustain its locomotion and perform tasks despite sudden joint actuator faults, relying exclusively on its internal sensors. Empirical evaluations on the Unitree A1 platform validate the deployability and effectiveness of Adapt on real-world quadruped robots, and affirm the robustness and practicality of our approach.

翻译：四足机器人对极端环境具有强适应性，但也会发生故障。一旦发生故障，机器人必须修复后才能返回任务，降低了其实际可行性。执行器退化是常见故障之一，源于设备老化或意外操作事件。传统上，解决此问题高度依赖复杂的容错设计，这要求开发者具备深厚的领域知识且缺乏泛化能力。基于学习的方法为缓解这些局限提供了有效途径，但如何在真实四足机器人上有效部署此类方法仍存在研究空白。本文提出了一种开创性的基于强化学习的师生框架，命名为执行器退化适应变换器（ADAPT），旨在填补这一研究空白。该框架生成统一控制策略，使机器人能仅依靠内部传感器，在突发关节执行器故障时维持运动并执行任务。在宇树A1平台上的实证评估验证了ADAPT在真实四足机器人上的可部署性和有效性，并确认了我们方法的鲁棒性和实用性。