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Prioritizing resource-aware grasps, where finger usage is explicitly modeled, dramatically improves the success rate of sequential dexterous manipulation tasks.
Robots get a crucial boost in robustness by learning to "see" and predict how objects will move, not just react to the current frame.
Teaching robots to manipulate objects just got easier: OCRA learns directly from human demonstration videos by focusing on object interactions and incorporating tactile feedback.
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.
