Learning from Fleet Experience: A Collective Learning Paradigm for Robots
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 an
| Authors | Chao Yu, Shu'ang Yu, Hao Lin et al. |
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| Published | 19 October 2026 |
| Last updated at source | — |
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