用强化学习+Transformer实时自适应，让四旋翼在电机故障后仍能稳定飞行
Reinforcement Learning-based Fault-Tolerant Control for Quadrotor with Online Transformer Adaptation
- 引入Transformer在线学习系统动态，无需重新训练即可应对未知故障
- 仿真中故障后95%任务成功，定位误差仅0.129米，优于现有方法
- 适合需要高可靠性的无人机任务，如救援、巡检等复杂环境
多旋翼在各类机器人应用中至关重要，但对执行器故障极为敏感，易引发快速失稳并降低任务可靠性。尽管已有多种基于强化学习（RL）的容错控制（FTC）方法被研究，但多数需预先掌握多旋翼模型或难以适应新配置。为此，我们提出一种新型混合式RL-FTC框架，集成基于Transformer的在线自适应模块。该框架利用Transformer实时推断隐状态表示，实现对未见过的系统模型的自适应，无需重新训练。我们在PyBullet仿真中评估了电机效能下降故障场景，取得95%的任务成功率和0.129米的位置均方根误差（RMSE），优于现有自适应方法（86%成功率，RMSE 0.153米）。进一步在不同配置的四旋翼上验证，证明了框架在未经训练动力学下的鲁棒性。结果表明，该框架显著提升了多旋翼的自适应与可靠性，适用于动态不确定环境中的高效故障管理。
原文摘要 · Abstract (English)
Multirotors play a significant role in diverse field robotics applications but remain highly susceptible to actuator failures, leading to rapid instability and compromised mission reliability. While various fault-tolerant control (FTC) strategies using reinforcement learning (RL) have been widely explored, most previous approaches require prior knowledge of the multirotor model or struggle to adapt to new configurations. To address these limitations, we propose a novel hybrid RL-based FTC framework integrated with a transformer-based online adaptation module. Our framework leverages a transformer architecture to infer latent representations in real time, enabling adaptation to previously unseen system models without retraining. We evaluate our method in a PyBullet simulation under loss-of-effectiveness actuator faults, achieving a 95% success rate and a positional root mean square error (RMSE) of 0.129 m, outperforming existing adaptation methods with 86% success and an RMSE of 0.153 m. Further evaluations on quadrotors with varying configurations confirm the robustness of our framework across untrained dynamics. These results demonstrate the potential of our framework to enhance the adaptability and reliability of multirotors, enabling efficient fault management in dynamic and uncertain environments. Website is available at http://00dhkim.me/paper/rl-ftc
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