机器人不再需要老师：靠同行的失败自学成才
现在的机器人学新技能，基本靠人盯着、出错了纠正它。但一个机器人车队动辄几十上百台，人根本盯不过来。这篇提出一个反直觉的思路：让机器人自己当老师。部署中的机器人把执行过程存下来，一旦某个机器人失败，系统就拿着这个失败场景，去车队里其他机器人的成功经验里找相似片段，用那些成功动作来修正自己的策略——全程不需要任何额外的人工示范或纠正。他们用 96 台机器人做手机包装的多阶段任务，成功率从 79.2% 提到 98.3%;在五个真实操作任务上，没参与训练数据采集的机器人也能达到 93.3% 到 99.2% 的成功率。更关键的是，车队越大，经验越多，效果越好——这意味着机器人学习可以靠规模堆出来，而不是靠人堆出来。它不是你明天就能用上的东西，但它是机器人从「实验室里被教」走向「部署中自学」的一个真实拐点。
📄 原文摘要(英文)
Experience generated during deployment offers an opportunity for robots to improve policy reliability through learning from their own executions. However, learning through human corrections requires teleoperators to monitor execution and intervene when policies fail, a demand that is difficult to meet as robot fleets grow. We introduce collective experience learning (CEL), a learning paradigm in which deployed robots share their execution experience to improve a common policy. We realize CEL through failure-guided experience retrieval fine-tuning (FERT), which uses failure contexts from robot executions to retrieve relevant trajectory segments from the fleet's shared successful experience and uses their actions as supervision for policy updates, without collecting additional expert demonstrations or corrective actions. A fault-tolerant asynchronous training system (FATS) coordinates experience collection, policy optimization and policy distribution despite local interruptions within the robot fleet. Across five real-world manipulation tasks, the resulting policies achieve success rates of 93.3-99.2%, evaluated over 120 trials per task on robots excluded from training data collection. With collection time per robot held fixed, larger fleets provide more experience and FERT generally achieves higher success rates across the evaluated sizes. On multi-stage phone packaging, learning with 96 robots increases success from 79.2% to 98.3%. These results support collective experience as a practical basis for scalable robot learning and a path towards improving policy reliability during deployment without collecting additional expert corrective actions.