← Tuesday, October 20, 2026Models & trainingPaper
Robot fleets learn from shared successful trajectories without new demonstrations
FERT retrieves successful fleet trajectories relevant to execution failures and uses them to update a shared policy; an asynchronous fault-tolerant system coordinates collection, training and distribution. Across five manipulation tasks, policies reach 93.3–99.2% success over 120 trials per task on robots excluded from training data.
- What
- Collective experience learning lets deployed robots share execution experience to improve a common policy without new expert demonstrations or corrections.
- Method
- FERT retrieves successful trajectory segments relevant to failure contexts and uses their actions to supervise policy updates; FATS coordinates collection, optimization, and distribution.
- Why it matters
- Across five manipulation tasks, policies reached 93.3–99.2% success, and larger fleets generally achieved higher success rates with fixed collection time per robot.
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