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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.
Overcoming the data scarcity bottleneck in robotic arm-hand coordination, FAR-Dex achieves over 80% real-world success in fine-grained dexterous manipulation tasks.
