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Quadrupedal robots can now skate circles around traditional designs, thanks to a co-design approach that unlocks dynamic maneuvers like hockey stops and self-alignment.
Robots can learn faster and generalize better by encoding dynamics directly into their neural network architecture, outperforming standard transformers and GNNs.
Achieve natural and dynamically feasible humanoid robot motion by retargeting human motion data with a skeleton-aligned approach, significantly reducing inverse kinematics error compared to task-space methods.
