FERT

Headlines · 2

1. 2d
   Tsinghua, Infinigence AI and AgiBot propose Collective Experience Learning for 96 robots

   Tsinghua University, Infinigence AI (无问芯穹) and AgiBot jointly released the Collective Experience Learning (CEL) paradigm, letting robots learn from peers' real task trajectories instead of relying only on human demonstrations. CEL pairs the FERT failure-guided experience-retrieval fine-tuning algorithm with the FATS fault-tolerant asynchronous training system, built on the open-source RLinf framework to coordinate up to 96 real robots. According to the project team, CEL surpassed the average performance of six human remote operators across five real tasks (flower arranging, folding clothes, phone charging, stool assembly and phone packaging), and on phone packaging the success rate rose from 86.7% with 16 training robots to 98.3% with 96.

   量子位（原生 RSS） · 🔥 7
2. Oct 18
   Robot fleets learn from shared experience, reaching 98.3% on phone packaging

   An arXiv preprint from a team at Tsinghua University, AgiBot and Infinigence AI introduces collective experience learning (CEL), a paradigm in which deployed robots share execution experience to improve one common policy. Its algorithm, failure-guided experience retrieval fine-tuning (FERT), uses the context of a failed execution to retrieve relevant successful trajectory segments from the fleet's shared experience and trains on their actions, requiring no extra expert demonstrations or corrective actions — humans only label success or failure and reset the environment. A fault-tolerant asynchronous training system (FATS) keeps collection, optimization and distribution running through individual robot interruptions. The authors report 93.3–99.2% success across five real-world manipulation tasks over 120 trials each on robots excluded from training data collection, and a rise from 79.2% to 98.3% on multi-stage phone packaging when learning with 96 robots, while noting evaluation was limited to five task-specific policies on a single platform.

   Hugging Face · Papers · 🔥 0

Experience and discussion from the community

Share my FERT experienceAsk about FERT