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arXiv · 2610.35604

RoboDoctor: Training-Free Recursive Self-Improvement for Robot Policies

Heyang Yu·Haomin Li·Sihang Li·Jixin Yan·Shiyun Peng·Wanqi Zhong·Tianyu Xin·Yuan Yao·Xiaoyu Tian·Yiming Li
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Abstract

Generalist robot policies and language-model agents provide complementary capabilities, but completing a task also requires adapting their use to the consequences of physical interaction. We study execution as a locus of adaptation: feedback guides the current action and supplies evidence for revising subsequent decisions under fixed model weights. We introduce RoboDoctor, a training-free framework that connects diagnosis, grounded action, and persistent experience across three revision targets. The harness allocates control between policy and agent; intervention determines when to assist while preserving useful policy behavior; and operation refines physical methods through observed effects. Linked experience records retain the reason for a decision, the attempted method, its outcome, and conditions for reuse. On a curated archive of 54 RoboDojo-Sim task entries, RoboDoctor achieves a five-dimension mean score of 57.10, with a native success rate of 49.44%. Recorded interactions illustrate strategy changes, selective correction, and grasp revision. These findings characterize execution-time adaptation in the evaluated system; controlled trials remain necessary to isolate the contribution of cross-episode experience.

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Heyang Yu, Haomin Li, Sihang Li, Jixin Yan, Shiyun Peng, Wanqi Zhong, Tianyu Xin, Yuan Yao, Xiaoyu Tian, Yiming Li. 2026-10-26. RoboDoctor: Training-Free Recursive Self-Improvement for Robot Policies. https://arxiv.org/abs/2610.35604

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