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Real-world tennis serving by humanoid robots is now possible without motion capture, thanks to a novel adaptive framework that learns directly from video.
By leveraging a structured latent space, RoboStriker achieves superior tactical performance in humanoid boxing, outperforming traditional methods that struggle with physical feasibility.
Coordinated scaling of Behavior Foundation Models can enhance humanoid robot control performance, achieving up to 82% error reduction in real-world tasks.
ReactiveBFM enables humanoids to achieve zero-shot moving target reaching with unprecedented agility and real-time adaptability.
