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Prioritizing resource-aware grasps, where finger usage is explicitly modeled, dramatically improves the success rate of sequential dexterous manipulation tasks.
Skip the costly human annotations: PromptEcho distills reward signals directly from frozen VLMs to boost text-to-image RL, achieving state-of-the-art results without any reward model training.
Forget end-to-end training: DexMulti's "retrieve-align-execute" approach lets robots master complex, multi-stage dexterous tasks from just a handful of demonstrations.
