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.

该论文引入一种基于强化学习的先导教师 - 学生框架，称为 Actuator Degradation Adaptation Transformer（ADAPT），旨在解决实际部署四足机器人上的问题。这个框架产生了一种统一的控制策略，使机器人能够在关节执行器突然故障的情况下维持其运动，并在依赖于内部传感器的情况下执行任务。对 Unitree A1 平台进行的实证评估验证了 ADAPT 在真实环境中对四足机器人的可部署性和有效性，并证实了我们方法的鲁棒性和实用性。