Collective Experience Learning (CEL)

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   Tsinghua, Infinigence AI and AgiBot propose Collective Experience Learning for 96 robots

   Tsinghua University, Infinigence AI (无问芯穹) and AgiBot jointly released the Collective Experience Learning (CEL) paradigm, letting robots learn from peers' real task trajectories instead of relying only on human demonstrations. CEL pairs the FERT failure-guided experience-retrieval fine-tuning algorithm with the FATS fault-tolerant asynchronous training system, built on the open-source RLinf framework to coordinate up to 96 real robots. According to the project team, CEL surpassed the average performance of six human remote operators across five real tasks (flower arranging, folding clothes, phone charging, stool assembly and phone packaging), and on phone packaging the success rate rose from 86.7% with 16 training robots to 98.3% with 96.

   量子位（原生 RSS） · 🔥 6

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