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A single robot can seamlessly transition between aerial flight and ground hopping, optimizing energy use and agility in real-time.
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.
