從機隊經驗中學習：機械人的集體學習範式
Learning from Fleet Experience: A Collective Learning Paradigm for Robots
研究提出集體經驗學習（CEL），讓部署中的機械人共享執行經驗，改善共同策略而毋須額外專家示範或糾正動作。透過失敗引導經驗檢索微調（FERT）及容錯非同步訓練系統（FATS），五項真實操作任務的成功率達 93.3-99.2%，每項以 120 次試驗評估。在多階段手機包裝任務中，96 個機械人令成功率由 79.2% 升至 98.3%。
Published on Oct 19
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Abstract
Robots share execution experience across fleets to improve policies via failure-guided retrieval and asynchronous training, achieving high success without extra expert corrections.
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.
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