Few-Shot Residual Adaptation of Pretrained Latent World Models
Overview
This paper introduces a few-shot residual adaptation approach for pretrained latent world models that learns self-supervised corrections from latent prediction errors without modifying pretrained weights. The method applies corrections inside the latent predictor and directly to its predicted latent state while keeping the model frozen. Evaluated in simulation and on a real robot under center-of-mass and friction shifts, the approach recovers 90% success under center-of-mass shift and 100% success under modified friction using three interaction episodes for adaptation.
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- arXiv Robotics — research abstractsSignalFew-Shot Residual Adaptation of Pretrained Latent World Models
This paper introduces a few-shot residual adaptation approach for pretrained latent world models that learns self-supervised corrections from latent prediction errors without modifying pretrained weights. The method applies corrections inside the latent predictor and directly to its predicted latent state while keeping the model frozen. Evaluated in simulation and on a real robot under center-of-mass and friction shifts, the approach recovers 90% success under center-of-mass shift and 100% success under modified friction using three interaction episodes for adaptation.
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