机器人集群经验共享学习：CEL 与 FERT 范式
Learning from Fleet Experience: A Collective Learning Paradigm for Robots
论文提出集体经验学习（CEL）范式，让部署中的机器人共享执行经验来改进同一策略，并通过失败引导经验检索微调（FERT）实现，无需额外专家演示或纠正动作。配套的容错异步训练系统（FATS）协调经验收集、策略优化与分发。在 5 个真实操作任务中，策略成功率达 93.3%-99.2%；多阶段手机包装任务上，96 台机器人学习使成功率从 79.2% 提升至 98.3%。
Computer Science > Robotics
arXiv:2610.25736 (cs)
Authors:Chao Yu, Shu'ang Yu, Hao Lin, Hongzhi Zang, Yuxiang Yan, Chenxi Jiang, Zhen Guo, Chuankang Li, Yihao Chen, Xin Xu, Xu Fu, Jiakai Zhou, Liangzhi Shi, Hongyi Ren, Yiwei Liu, Jing Wu, Yuanqing Wang, Chen Wu, Bo Dai, Chunyu Yang, Hongliang Huang, Shuoyu Yue, Weiqi Yin, Yinuo Chen, Ruihua Chen, Chunyang Zhu, Han Xiao, Yanghao Li, Ruipu Wu, Zhikai Cai, Xiu Chen, Xu Tan, Yuchen Yan, Jialin Zhang, Wen Zeng, Yiming Zhou, Zixiao Huang, Guanghui Ren, Quanlu Zhang, Boxun Li, Maoqing Yao, Yu Wang
Abstract: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.
| Subjects: | Robotics (cs.RO) |
| Cite as: | arXiv:2610.25736 [cs.RO] |
| (or arXiv:2610.25736v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.25736 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shu-Ang Yu [view email]
[v1]
Mon, 19 Oct 2026 03:41:27 UTC (14,558 KB)
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来源：HuggingFace Daily Papers · arxiv.org