AgiBot, together with Tsinghua University and Infinigence AI, releases the CEL collective experience learning paradigm
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.
Why read this
AI score 62
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.
Topics
Body · AI translation
People's Finance News, October 30 (Xinhua) — Recently, AgiBot, together with Tsinghua University and Infinigence AI, launched the "Collective Experience Learning" (CEL) paradigm. Relying on the FERT failure-guided experience retrieval algorithm and the FATS fault-tolerant asynchronous training system, a cluster of 96 real robots achieved "robots teaching robots," breaking free from a heavy dependence on demonstration teaching by human experts. In tests on five real-world tasks, including flower arranging, clothes folding, and mobile phone packaging, the CEL approach comprehensively outperformed traditional training methods, with its overall success rate reaching up to 16.7 percentage points higher than the average level of human teleoperators; under the same training duration, the larger the cluster scale, the higher the robots' operational reliability, breaking the industry bottleneck of "insufficient supply of human demonstrations" for the large-scale deployment of embodied intelligence.