# RoboDoctor: A Training-Free Recursive Self-Improvement Framework for Robot Policies

- Source: arXiv cs.RO
- Published: 2026-10-27T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/q50kpv5tmxnkpg60c9n3ct0jn
- Original: https://arxiv.org/abs/2610.35604

## Summary

RoboDoctor is a training-free framework that, under fixed model weights, lets generalist robot policies and language-model agents adapt during execution through three revision targets: diagnosis, action execution, and persistent experience records. On 54 RoboDojo-Sim task entries, the framework achieved a five-dimension mean score of 57.10 and a native success rate of 49.44%; recorded interactions show policy switching, selective correction, and grasp revision. The authors note that these results characterize execution-time adaptation in the evaluated system and that controlled experiments are still needed to isolate the contribution of cross-episode experience.