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.

翻译：适应不可预测的损伤对于自主腿式机器人至关重要，然而现有基于多策略或元学习框架的方法面临泛化能力有限和维护复杂等挑战。为解决这一问题，我们首先分析并总结了八类损伤场景，包括传感器故障与关节功能障碍。随后，我们提出一种新颖的无模型两阶段训练框架——统一故障控制器（UMC），通过引入掩码机制以增强损伤弹性。具体而言，模型首先在正常环境中训练，以确保其在标准条件下的鲁棒性能。在第二阶段，我们利用掩码阻止腿式机器人依赖故障肢体，从而在发生故障时实现自适应步态与运动调整。实验结果表明，在三种运动任务中，我们的方法将Transformer的任务完成能力平均提升了36%，MLP平均提升了39%。源代码与训练模型将向公众公开。