用模块化专家系统提升机器人故障下的行走能力
Mixture-of-Experts RL for Fault-Tolerant Legged Locomotion
- 根据故障类型激活不同控制专家，实现精准应对
- 在多种故障下性能优于传统单一策略，且占用更少算力
- 适合计算资源受限的航天机器人等场景
在行星探测等远程环境中部署的腿式机器人，必须在执行器故障和复杂地形下仍保持可靠行走。尽管强化学习已在腿式机器人行走中取得显著成果，但单一策略难以高效表示应对不同故障所需的多样化控制方法。本文提出一种故障感知的模块化控制架构，利用故障诊断信息显式激活与特定执行器故障模式对应的专用控制专家。实验表明，这种条件化的模块化策略在同等规模下始终优于单一策略，在各类故障场景中均实现更高行走性能。此外，该架构在显著降低网络容量的情况下仍保持竞争力，凸显其在计算资源受限的机器人平台（如太空任务常用设备）中的适用性。代码已开源：https://github.com/iit-DLSLab/fault-locomotion-isaaclab。
原文摘要 · Abstract (English)
Legged robots deployed in planetary exploration and other remote environments must maintain reliable locomotion despite actuator failures and challenging terrain conditions. Although reinforcement learning has achieved strong results in legged locomotion, monolithic policies can struggle to efficiently represent the diverse control strategies required to compensate for different fault conditions. In this work, we propose a fault-aware modular control architecture that explicitly leverages fault-diagnosis information to activate specialized control experts associated with distinct actuator failure modes. Experimental results show that explicit fault-conditioned modular policies consistently outperform monolithic policies of comparable size, achieving higher locomotion performance across failure scenarios. Moreover, the proposed modular architecture retains competitive performance even under significantly reduced network capacity, highlighting its suitability for compute-constrained robotic platforms, such as those typically employed in space applications. The code associated with this work is available at: https://github.com/iit-DLSLab/fault-locomotion-isaaclab.
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