Adaptive Control Strategy for Quadruped Robots in Actuator Degradation
  Scenarios
Xinyuan Wu Wentao Dong Hang Lai Yong Yu Ying Wen
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.