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ProxiDex is presented, a dynamics-guided proximity policy framework that treats hand-object proximity as an interaction state for dexterous manipulation, and adaptively reweights proximity tokens across manipulation phases and uses dynamics-consistency supervision to guide policy inference, stabilizing action generation under unreliable visual feedback.
Zero-shot reward function design using CRWM cuts down design latency while achieving state-of-the-art performance in robotic skill acquisition.
A novel approach to anomaly localization that significantly improves detection of subtle defects in industrial products by leveraging multimodal large language models.
