robotics
A Deployed Robot Kept Learning Without Gradients
CFAM stores verified near-out-of-distribution experience as one-shot competence capsules while freezing its slower learned core.
Summary
CFAM stores verified near-out-of-distribution experience as one-shot competence capsules while freezing its slower learned core.
Across five embodiments, the authors report matching a standard policy's operating point with 40 percent of the prior training data. Autonomous near-OOD capture improved action success by 13.9 points, while sequential simulation showed less backward loss than LoRA. Open-world novelty remains outside scope.
Why it matters
CFAM stores verified near-out-of-distribution experience as one-shot competence capsules while freezing its slower learned core.
Limits and context
No additional limitation was separately recorded.
Key claims
CFAM stores verified near-out-of-distribution experience as one-shot competence capsules while freezing its slower learned core.
Evidence: source-2026-09-07-018
Sources
- arXiv preprint 2609.04552arXiv · primary research
Corrections
No corrections have been recorded for this story.