From learning from humans to learning from robot companions, Tsinghua University, together with Infinigence AI and AgiBot, proposes a new path for the large-scale deployment of embodied intelligence

What happened: Tsinghua University, Infinigence AI and AgiBot jointly proposed Collective Experience Learning (CEL), a paradigm where robots learn from other robots' past experience rather than relying only on human demonstration, validated on a cluster of 96 real robots. Key numbers: Two untrained robots ran 600 tests Why it matters: The authors frame the shortage of experienced human operators as a bottleneck for scaling embodied intelligence, and present CEL as turning robot scale into an advantage, with the three parties covering research, AI infrastructure and real-world deployment. Coverage: 3 outlets in 24h

Tsinghua University, Infinigence AI and AgiBot jointly proposed Collective Experience Learning (CEL), a paradigm where robots learn from other robots' past experience rather than relying only on human demonstration, validated on a cluster of 96 real robots.

The authors frame the shortage of experienced human operators as a bottleneck for scaling embodied intelligence, and present CEL as turning robot scale into an advantage, with the three parties covering research, AI infrastructure and real-world deployment.