AgiBot, together with Tsinghua University and Infinigence AI, releases the CEL collective experience learning paradigm
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AgiBot, together with Tsinghua University and Infinigence AI, has introduced the Collective Experience Learning (CEL) paradigm. Built on the FERT failure-guided experience retrieval algorithm and the FATS fault-tolerant asynchronous training system, it uses a cluster of 96 real robots to enable robots to teach robots, breaking free from a heavy reliance on demonstrations by human experts. In tests on 5 real-world tasks including flower arranging, clothes folding, and phone packaging, the CEL approach achieved an overall success rate up to 16.7 percentage points higher than the average level of human teleoperators, and with the same training duration, the larger the cluster size, the higher the operational reliability.
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The original text provides a success-rate comparison across a cluster of 96 real robots and 5 tasks, showing how collective experience learning can reduce reliance on human demonstrations.
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人民财讯10月30日电，近日，智元联合清华大学、无问芯穹推出“群体经验学习”（Collective Experience Learning，CEL）范式，依托FERT失败引导经验检索算法与FATS容错异步训练系统，96台真机集群实现“机器人教机器人”，摆脱对人类专家示教的强依赖。在插花、叠衣、手机包装等5项真实任务测试中，CEL方案表现全面超越传统训练方法，整体成功率最高较人类远程操作员平均水平高出16.7个百分点；同等训练时长下，集群规模越大，机器人作业可靠性越高，为具身智能大规模落地破除 “人类示范供给不足” 的行业瓶颈。