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Tsinghua, Infinigence AI, and AgiBot Test Collective Experience Learning Across 96 Robots

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Tsinghua University, Infinigence AI, and AgiBot proposed Collective Experience Learning (CEL) and tested it across 96 real robots; according to Qbit, two robots excluded from training achieved 97.5% overall success across 600 tests covering five tasks. CEL lets robots learn from other robots’ successful operations. It combines a fault-tolerant asynchronous training system built on RLinf with failure-guided experience retrieval and fine-tuning, using past successes to target improvements where robots fail.

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Latest development10/30 11:48 (UTC+8)
Tsinghua and partners report collective robot learning results across 96 real robots量子位

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10/30
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    Tsinghua and partners report collective robot learning results across 96 real robots

    Tsinghua University and its partners proposed Collective Experience Learning (CEL), which lets robots learn from peers’ successful experiences, and validated it across 96 real robots. Two robots excluded from training achieved 97.5% overall success across 600 tests covering five tasks. The approach combines a fault-tolerant asynchronous training system built on RLinf with failure-guided experience retrieval and fine-tuning to target improvements using past successful operations.

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