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

- Source: HuggingFace Daily Papers
- Published: 2026-10-19T00:00:00.000Z
- RoboHot: https://robohot.news/en/items/zbgepd3v2a3duo41i8dpjun9r
- Original: https://huggingface.co/papers/2610.25736

## Summary

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.