# Learning from Fleet Experience: Collective Experience Learning Across 96 Robots

- Source: arXiv cs.RO
- Published: 2026-10-22T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/imi9tkemgad77rfe6hy2gokmn
- Original: https://arxiv.org/abs/2610.25736

## Summary

The paper proposes the Collective Experience Learning (CEL) paradigm, in which deployed robots pool their own execution experience to update a shared policy that is then returned to the fleet for use. It is implemented through failure-guided experience retrieval fine-tuning (FERT), which uses failure contexts to retrieve relevant segments from successful trajectories and prioritizes their recorded actions for supervised updates, without additional expert demonstrations or corrective actions; a supporting fault-tolerant asynchronous training system (FATS) coordinates robot execution, cloud inference, and policy optimization.

## Why it matters

The paper proposes replacing expert corrections with the deployed robots' own execution experience to iterate on the policy, and reports real-world manipulation results at a scale of 96 robots.