机器人老是失败？试试FAR，它让机器人从失败自学习，不用人管。仿真和真实环境都提升10%以上。
FAR（Failure-Aware Retry）是一种让机器人在真实环境中从失败中学习的框架。它通过失败对比偏好适应（Failure-Contrastive Preference Adaptation）从失败数据中构造偏好学习，引导策略避开先前失败行为。在重试期间加入轻量动作扰动促进局部探索。在仿真和真实操作任务中，FAR相比标准Diffusion Policy平均成功率提升17.6%（仿真）和11.7%（真实）。在持续策略改进中，FAR通过利用信息性失败案例显著提升数据效率。
FAR: Failure-Aware Retry for Test-Time Recovery and Continual Policy Improvement
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.