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On October 30, 2026, AgiBot, together with Tsinghua University and Infinigence AI, released the Collective Experience Learning (CEL) paradigm, which enables robots to use their peers' real task trajectories as learning material, with humans only responsible for judging the outcomes. The paradigm is accompanied by the FERT failure-guided experience retrieval algorithm and the FATS fault-tolerant asynchronous training system. On five real-robot tasks—flower arrangement, clothes folding, phone charging, stool assembly, and phone packaging—the policies trained with CEL all exceeded the average level of six human teleoperators, leading by up to 16.7 percentage points, with an overall success rate of 97.5%. 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%. In the 96-robot experiment, FATS completed a cumulative 9,834 rounds of task collection, and the machine-time loss caused by 30 failures accounted for only 1.3% of the cluster's total operating time.
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Oct 30
Leitech
AgiBot, together with Tsinghua University and Infinigence AI, releases the CEL collective experience learning paradigm
AgiBot, together with Tsinghua University and Infinigence AI, has released the collective experience learning paradigm CEL, which enables robots to use their peers' real operational trajectories as learning material, with humans only responsible for judging outcomes. On five real-machine tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—the policies trained with CEL all exceeded the average level of 6 human teleoperators, leading by up to 16.7 percentage points, with an overall success rate of 97.5%; 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%. The accompanying FERT algorithm uses failure trajectories to identify weaknesses and retrieves successful segments for prioritized sampling, while the FATS fault-tolerant asynchronous training system completed a cumulative 9,834 rounds of task collection in the 96-robot experiment, with the machine-time loss caused by 30 failures accounting for only 1.3% of the cluster's total operating time.
Securities Times
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.
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Shanghai Observer
Tsinghua University, Infinigence AI, and AgiBot propose CEL collective experience learning, with 96 robots verifying a 97.5% success rate
Tsinghua University, Infinigence AI, and AgiBot jointly proposed a new paradigm called Collective Experience Learning (CEL) and completed large-scale validation in a cluster of 96 real robots. Two robots that did not participate in training completed 600 tests 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%; the success rates on all five tasks exceeded the average level of 6 human operators. The success rate on the phone packaging task rose from 79.2% to 98.3%, and when the number of robots participating in training increased from 16 to 96, the success rate rose from 86.7% to 98.3%.
The Beijing News
Tsinghua University, Infinigence AI, and AgiBot jointly propose a collective experience learning paradigm, validated with 96 robots
Tsinghua University, Infinigence AI, and AgiBot have jointly proposed a new paradigm called Collective Experience Learning (CEL) and completed large-scale validation in a cluster of 96 real robots. The experiments covered five tasks: flower arrangement, clothes folding, phone charging, stool assembly, and phone packaging. Two robots that did not participate in training completed 600 test trials with 15 failures, for an overall success rate of 97.5%, and the success rates on all five tasks exceeded the average level of six human operators. The success rate on the phone packaging task rose from 79.2% to 98.3%; when the number of robots participating in training increased from 16 to 96, the success rate rose from 86.7% to 98.3%.
The Beijing NewsRelated discussion
The Beijing News joined this discussion
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Wallstreetcn
Tsinghua, Infinigence AI, and AgiBot propose the CEL collective experience learning paradigm, with a 97.5% success rate verified on a 96-robot cluster
Tsinghua University, Infinigence AI, and AgiBot jointly propose a new paradigm called Collective Experience Learning (CEL), which enables robots to reuse the experience of their peers rather than relying on manual teaching, and have completed large-scale validation on a cluster of 96 real robots. Two robots that did not participate in training completed a total of 600 tests with 15 failures, for an overall success rate of 97.5%; on all five tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—the success rate exceeded the average level of 6 human operators. At the system level, they built the fault-tolerant asynchronous training system FATS based on RLinf; in the 96-robot scale test, a cumulative 9,834 rounds of execution experience were collected, 22 robots experienced 30 failures, and machine-time loss accounted for only 1.3%. At the algorithm level, they propose the failure-guided experience retrieval fine-tuning algorithm FERT, which uses both successful and failed experience to locate error-prone steps. The hardware is the AgiBot G2 dual-arm robot, and the base model is π0.
QbitAI
Tsinghua University, together with Infinigence AI and AgiBot, proposes Collective Experience Learning (CEL); 96 robots validate a 97.5% success rate
Tsinghua University, Infinigence AI, and AgiBot jointly propose the Collective Experience Learning (CEL) paradigm, enabling robots to learn from their peers' successful experiences rather than relying solely on human demonstrations, and complete large-scale validation in a cluster of 96 real robots. Two robots that did not participate in training were tested a total of 600 times across five tasks—flower arranging, clothes folding, phone charging, stool assembly, and phone packaging—with 15 failures and an overall success rate of 97.5%, all exceeding the average level of 6 human operators; the success rate for the phone packaging task increased from 79.2% to 98.3%. When the number of robots participating in training increased from 16 to 96, the success rate rose from 86.7% to 98.3%. At the system level, a fault-tolerant asynchronous training system, FATS, was built based on the open-source framework RLinf; in the 96-robot scale test, a cumulative 9,834 rounds of execution experience were collected, 22 robots experienced 30 failures, and the time loss caused by failures accounted for only 1.3% of total operating machine-hours.
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