发表机构
Institute of Trustworthy Embodied AI (TEAI), Fudan University; Shanghai Key Laboratory of Multimodal Embodied AI（复旦大学可信具身人工智能研究所; 上海多模态具身人工智能重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何让具身机器人从故障中恢复，提出EgoRecovery协同训练框架，利用以自我为中心的人类数据捕捉故障恢复过程，将人类恢复演示与机器人数据共享空间对齐，实验证明该方法在现实世界恢复任务中比多种基线方法成功率更高。
AI 中文摘要
强大的具身机器人应能从故障中恢复并重试任务，以在非结构化和嘈杂的现实世界环境中可靠运行。实现此能力需基于捕捉恢复行为的数据训练策略。通过机器人遥操作收集此类数据难以扩展，因为诱导不同故障状态、执行纠正措施和重置环境耗时。以自我为中心的人类数据捕捉故障恢复过程提供了可扩展的替代方案。我们提出EgoRecovery，一个用于学习恢复行为的协同训练框架，人类恢复演示与与机器人数据共享的紧凑纠正意图空间对齐。实验表明EgoRecovery比仅机器人恢复、直接与人类恢复数据协同训练和直接意图转移基线有更高成功率。
英文摘要
Robust embodied robots should be able to recover from failures and retry tasks in order to operate reliably in unstructured and noisy real-world environments. Achieving this capability requires training policies on data that captures recovery behaviors. However, collecting such data through robot teleoperation is difficult to scale, as it is time-consuming to induce diverse failure states, perform corrective actions, and reset the environment. This challenge is further exacerbated by the high diversity of failure modes, which demands substantially more recovery data than success demonstrations. In this work, we show that egocentric human data capturing failure recovery processes provides a scalable alternative. By efficiently arranging task-level failure configurations and recording short recovery segments, human operators can generate more than 10x as much valid recovery data per hour compared to robot teleoperation under our protocol. To address the embodiment gap between human and robot, we propose EgoRecovery, a co-training framework for learning recovery behavior, where human recovery demonstrations are aligned to a compact corrective-intent space shared with robot data, which captures the timing and magnitude of correction. Only a small number of robot recovery demonstrations are required to connect this intent to executable robot actions. At deployment, a learned recovery gate predicts when correction is needed from robot observations and activates the corrective intent only in recovery states. Experiments on real-world recovery tasks show that EgoRecovery improves success from failure starts over robot-only recovery, direct co-training with human recovery data, and direct intent-transfer baselines.
