用强化学习让机械臂在关节故障时仍能完成任务
Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning
- 基于部分可观测强化学习，动态补偿故障关节
- 在7自由度机械臂上实现93.6%的任务成功率
- 适合需要高可靠性的工业机器人场景
机械臂广泛应用于各类工业场景执行复杂重复任务，但易受突发硬件故障影响。本文针对关节失效问题，提出一种强化学习（RL）框架，实现任务执行中对非功能关节的自适应补偿。实验平台为具有7个自由度（DOFs）的Franka机械臂，将问题建模为部分可观测马尔可夫决策过程（POMDP），在多种关节故障条件下训练并测试，涵盖永久损坏与间歇性故障。通过与传统逆运动学控制方法对比，结果表明该方法在已见和未见故障场景下均能高效完成任务，平均成功率达93.6%，展现出强鲁棒性与适应能力。研究验证了强化学习提升机器人系统韧性与可靠性的潜力，使其更适用于不可预测环境。相关代码与模型已公开。
原文摘要 · Abstract (English)
Robotic manipulators are widely used in various industries for complex and repetitive tasks. However, they remain vulnerable to unexpected hardware failures. In this study, we address the challenge of enabling a robotic manipulator to complete tasks despite joint malfunctions. Specifically, we develop a reinforcement learning (RL) framework to adaptively compensate for a non-functional joint during task execution. Our experimental platform is the Franka robot with 7 degrees of freedom (DOFs). We formulate the problem as a partially observable Markov decision process (POMDP), where the robot is trained under various joint failure conditions and tested in both seen and unseen scenarios. We consider scenarios where a joint is permanently broken and where it functions intermittently. Additionally, we demonstrate the effectiveness of our approach by comparing it with traditional inverse kinematics-based control methods. The results show that the RL algorithm enables the robot to successfully complete tasks even with joint failures, achieving a high success rate with an average rate of 93.6%. This showcases its robustness and adaptability. Our findings highlight the potential of RL to enhance the resilience and reliability of robotic systems, making them better suited for unpredictable environments. All related codes and models are published online.
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