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A single egocentric human video can yield real-world dexterous manipulation policies that beat existing baselines by 3.5x, unlocked by treating video-to-sim translation as a closed-loop, agentic tool-synthesis problem rather than a rigid heuristic pipeline.
Achieving a 71% success rate in real-world dexterous manipulation across diverse objects, DemoMimic reveals the critical role of local contact geometry in enhancing sim-to-real transfer.
Visual tracks can double the success rates of robot tasks by providing a powerful interface between control and visual prediction.
