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SOLO achieves a remarkable 97.5% mean traversal success on complex terrains, showcasing a leap in humanoid locomotion capabilities.
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.
Humanoid robots can now learn complex, terrain-aware motions directly from video using a low-cost pipeline, eliminating the need for expensive MoCap data and manual motion design.
