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

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.

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