机器人出错后能自主学习改进，不再重复失败。
FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement
- 从失败中构建偏好数据，引导策略避开错误动作
- 测试时成功率提升17.6%（仿真）和11.7%（现实）
- 适合需要持续优化的机器人部署场景
机器人在真实环境中部署时常遭遇失败。盲目重试会重复错误，而现有恢复方法多依赖人工干预。本文提出失败感知重试框架FAR，使机器人能在测试阶段学习过往失败经验，自适应调整行为并最终自主完成任务。FAR结合失败对比偏好适配机制，利用失败案例构建偏好学习数据以规避旧错误行为，并通过轻量级动作扰动促进局部探索。同时将成功恢复轨迹引入训练循环，实现持续策略优化。仿真与真实机械臂任务实验表明，相比标准扩散策略，FAR在仿真中平均成功率提升17.6%，真实场景提升11.7%。此外，在重置和时间步预算限制下，FAR显著提升数据效率，充分挖掘有信息量的失败案例。
原文摘要 · Abstract (English)
Robot policies inevitably encounter failures when deployed in real environments. Naive retries often repeat the same mistakes, while many existing recovery methods rely on human intervention. In this paper, we propose Failure-Aware Retry (FAR), a framework that enables robots to learn from previous failures at test time, adapt their behavior accordingly, and eventually complete the task autonomously. FAR combines Failure-Contrastive Preference Adaptation, which constructs preference learning data from failures to steer the policy away from previously unsuccessful behaviors, with lightweight action perturbations during retries to encourage local exploration. We further incorporate successful recovery trajectories into a training loop for continual policy improvement. Experiments in both simulation and real-world manipulation tasks show that FAR substantially improves success rates and robustness, with average gains of 17.6% over the standard diffusion policy in simulation and 11.7% in the real world. In addition, FAR significantly improves data efficiency under both reset and timestep budgets during continual policy improvement by exploiting informative failure cases.
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