提出统一控制器UMC，让机器人在关节故障时仍能自适应行走。
UMC: Unified Resilient Controller for Legged Robots with Joint Malfunctions
- 分两阶段训练，用掩码阻止依赖故障肢体，实现自适应
- 在3项任务中平均提升36%~39%的任务完成率
- 无需复杂模型，适合实际部署的足式机器人
适应不可预测的损伤对自主足式机器人至关重要，但现有基于多策略或元学习的方法存在泛化能力有限和维护复杂的问题。本文首先分析并总结了八类损伤场景，包括传感器故障和关节异常。随后提出一种新型无模型、两阶段训练框架——统一故障控制器（UMC），引入掩码机制以增强故障韧性。第一阶段在正常环境下训练，确保标准条件下的稳健性能；第二阶段通过掩码抑制故障肢体的使用，使机器人能在故障发生后自适应调整步态与运动。实验表明，该方法在三个移动任务中，对Transformer模型平均提升36%，对MLP模型平均提升39%的任务完成率。代码与训练模型将公开共享。
原文摘要 · Abstract (English)
Adaptation to unpredictable damages is crucial for autonomous legged robots, yet existing methods based on multi-policy or meta-learning frameworks face challenges like limited generalization and complex maintenance. To address this issue, we first analyze and summarize eight types of damage scenarios, including sensor failures and joint malfunctions. Then, we propose a novel, model-free, two-stage training framework, Unified Malfunction Controller (UMC), incorporating a masking mechanism to enhance damage resilience. Specifically, the model is initially trained with normal environments to ensure robust performance under standard conditions. In the second stage, we use masks to prevent the legged robot from relying on malfunctioning limbs, enabling adaptive gait and movement adjustments upon malfunction. Experimental results demonstrate that our approach improves the task completion capability by an average of 36% for the transformer and 39% for the MLP across three locomotion tasks. The source code and trained models will be made available to the public.
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