Zhiyuan, with Tsinghua University and Wuwen Xinqun, released a Collective Experience Learning (CEL) paradigm using a FERT failure-guided experience-retrieval algorithm and a FATS fault-tolerant asynchronous training system. A 96-robot physical cluster demonstrated “robot teaches robot,” reducing dependence on human expert demonstrations. In five real-world tasks including flower arranging, clothes folding and phone packaging, CEL outperformed conventional training methods, with peak overall succ

Zhiyuan, with Tsinghua University and Wuwen Xinqun, released a Collective Experience Learning (CEL) paradigm using a FERT failure-guided experience-retrieval algorithm and a FATS fault-tolerant asynchronous training system. A 96-robot physical cluster demonstrated “robot teaches robot,” reducing dependence on human expert demonstrations. In five real-world tasks including flower arranging, clothes folding and phone packaging, CEL outperformed conventional training methods, with peak overall success rates up to 16.7 percentage points above the average of human remote operators. With equal training time, larger cluster scale increased operational reliability. CEL is presented as addressing the industry bottleneck of insufficient human demonstration supply for large-scale embodied intelligence deployment.