Summary
UniT (Unified Latent Action Tokenizer via Visual Anchoring) addresses the bottleneck of scaling humanoid AI due to scarce robotic data. It proposes a framework to bridge the cross-embodiment gap between abundant human data and humanoid robots, despite kinematic mismatches. By establishing a "unified physical language" grounded in universal visual consequences, UniT aims to enable effective human-to-humanoid policy learning and world modeling.
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[Paper] Ruka-v2: Tendon Driven Open-Source Dexterous Hand with Wrist and Abduction for Robot Learning
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