机器人犯错后能自己爬起来，成功率涨了15个点
机器人干活最怕的不是不会，而是干到一半跑偏了，然后一路错到底。这篇 CARE 的思路是：让它把失败过程记下来，学自己是怎么跑偏的，下次再偏就知道怎么拉回来。做法很具体——把任务拆成阶段，记录每个阶段出错后的偏差规律，再拿这些真实失败数据生成纠正动作；执行时用 3D 视觉盯着，一旦发现偏离就触发小调整或重做，而不是从头再来。在多个模拟和真实双臂任务上，平均成功率提升了约15个点。它不是你明天就能装进自家机器人的东西，但这是机器人从「会做」走向「做不砸」的关键一步。
📄 原文摘要(英文)
Vision-Language-Action (VLA) policies achieve strong performance in robotic manipulation but remain brittle once execution deviates from nominal trajectories. We propose CARE (Corrective Atomic Robotic Execution), a framework that improves recovery by learning from failures encountered during execution. Instead of generating corrective data from manually designed or random perturbations, CARE collects failed rollouts, models stage-conditioned post-failure deviations, and uses the resulting empirical distributions to synthesize representative failure states and corrective demonstrations. At inference time, CARE combines stage-wise planning with physically grounded 3D monitoring to trigger atomic adjustments or re-operations while preserving task progress. We further introduce the Failure State Recovery Benchmark (FSR-Bench), which evaluates recovery from intermediate failure states under local deviations and structural anomalies. Experiments across multiple VLA backbones, simulation benchmarks, and real-world dual-arm tasks show consistent improvements, with average task-success gains of 14.5 points in simulation and 15.9 points in the real world. Code, models, and data are available at https://github.com/xiaojunlan/care