Article

Learning from Fleet Experience: Improving Robot Manipulation Policies with Shared Fleet Experience

The paper proposes the collective experience learning (CEL) paradigm, in which deployed robots share execution experience to improve a common policy without collecting additional expert demonstrations or corrective actions. It uses failure-guided experience retrieval fine-tuning (FERT) to retrieve relevant trajectory segments from the fleet's shared successful experience as supervision, and a fault-tolerant asynchronous training system (FATS) to coordinate experience collection, policy optimization, and distribution.

Part of the event: 96 robots share experience, raising operation success rate to 98.3%

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