Tsinghua, Infinigence AI, and AgiBot propose CEL, validated on 96 robots

Report timeline

1. Embodied AI Industry Weekly: Unitree's Net Profit Up 271% in First Three Quarters; AgiBot Releases Heavy-Load Robot Genie G2 Max

   Unitree Technology's Q3 2026 report shows revenue of 1.817 billion yuan for the first three quarters, up 55.71% year on year, and net profit attributable to shareholders of 387 million yuan, up 270.54% year on year; it is the company's first financial report since going public. In the same period, AgiBot released the Genie G2 Max, a safety-certified heavy-load embodied industrial robot, with a 20 kg payload per arm, 40 kg with both arms, and a 50 kg peak, aimed at depalletizing and palletizing, material transfer, and production-line feeding. Tsinghua University, Infinigence AI, and AgiBot jointly proposed a new paradigm called Collective Experience Learning (CEL) and completed large-scale validation on a cluster of 96 real robots.

   机器人产业应用 · 微信公众号 · Nov 1, 2026

2. Tsinghua-led team lets 96 robots share experience, raising phone packaging success rate to 98.3%; AgiBot releases GO-3 Preview

   A Tsinghua-led team enabled 96 robots to share manipulation experience, raising the success rate on a phone packaging task to 98.3%. AgiBot released GO-3 Preview, a unified framework connecting reasoning, prediction, and action.

   APPSO · 微信公众号 · Oct 31, 2026

3. Tsinghua-led team lets 96 robots share experience, raising phone-packaging success rate to 98.3%

   A Tsinghua-led team let 96 robots share experience, raising the phone-packaging success rate to 98.3%. AgiBot released GO-3 Preview, connecting reasoning, prediction, and action in a unified framework.

   爱范儿 · Oct 31, 2026

4. AgiBot, Tsinghua, and Infinigence AI Release CEL Collective Experience Learning Paradigm, Achieving Autonomous Cluster Evolution with 96 Real Robots

   On October 30, AgiBot, together with Tsinghua University and Infinigence AI, released the collective experience learning paradigm CEL, which draws on the real operational trajectories of robot peers as its source of experience, accompanied by the FERT failure-guided experience retrieval fine-tuning algorithm and the FATS fault-tolerant asynchronous training system. Across five real-world tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—CEL-trained policies all exceeded the average level of 6 human teleoperators, leading by up to 16.7 percentage points. In the phone packaging task, the number of robots participating in training expanded from 16 to 96, and the success rate rose from 86.7% to 98.3%.

   36Kr Embodied Intelligence · Oct 30, 2026

5. Infinigence AI, Tsinghua and AgiBot propose collective experience learning (CEL) paradigm, validated on 96 robots

   Infinigence AI, together with Tsinghua University and AgiBot, proposed the collective experience learning (CEL) paradigm and completed prototype validation on 96 robots. In a phone packaging task, a policy trained with CEL completed 120 tests on two robots of the same model that did not participate in training, with success rate rising from 79.2% to 98.3%; when the number of robots participating in learning increased from 16 to 96, the test success rate rose from 86.7% to 98.3%.

   Xbotics具身智能实验室 · 微信公众号 · Oct 30, 2026

6. Infinigence AI, Tsinghua and AgiBot propose Collective Experience Learning (CEL), validated on 96 robots with 98.3% success rate

   Infinigence AI, together with Tsinghua University and AgiBot, proposed the Collective Experience Learning (CEL) paradigm and completed prototype validation on 96 robots. In a phone packaging task, policies trained with CEL completed 120 tests on two robots of the same model that did not participate in training, with the success rate rising from 79.2% to 98.3%; when the number of robots participating in learning increased from 16 to 96, the test success rate rose from 86.7% to 98.3%.

   3D视觉工坊 · 微信公众号 · Oct 30, 2026

7. AgiBot and Tsinghua Release CEL Collective Experience Learning Paradigm, Achieving Autonomous Swarm Evolution with 96 Real Robots

   AgiBot, together with Tsinghua University and Infinigence AI, released the collective experience learning paradigm CEL, which lets robots use the success and failure trajectories of their peers as learning material, with humans only responsible for judging outcomes. Across five real-world tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—policies trained with CEL all exceeded the average level of 6 human teleoperators, leading by up to 16.7 percentage points; in the phone packaging task, the number of robots participating in training expanded from 16 to 96, and the success rate rose from 86.7% to 98.3%.

   雷科技 · Oct 30, 2026

8. Tsinghua, AgiBot and Infinigence AI propose Collective Experience Learning (CEL), validated on 96 real robots for scalable gains

   Tsinghua University, AgiBot and Infinigence AI jointly proposed the Collective Experience Learning (CEL) paradigm and completed prototype validation on 96 robots. In a phone-packaging task, the trained policy completed 120 tests on two robots of the same model that did not participate in training, raising the success rate from 79.2% to 98.3% and reducing failures from 25 to 2.

   机器之心 · 微信公众号 · Oct 30, 2026

9. Tsinghua University, Infinigence AI, and AgiBot propose collective experience learning CEL, validated on a 96-robot cluster

   Tsinghua University, Infinigence AI, and AgiBot jointly proposed the collective experience learning (CEL) paradigm, which lets robots share the successful experience of their peers rather than relying only on human demonstrations, and validated it on a cluster of 96 real robots. Two robots that did not participate in training were tested 600 times in total across five tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—with 15 failures, for an overall success rate of 97.5%, all exceeding the average level of 6 human operators; the success rate on the phone packaging task rose from 79.2% to 98.3%.

   量子位 · Oct 30, 2026