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Treating action conditioning as a structured process rather than a global compression could redefine how we model high-dimensional dexterous actions in AI.
Achieving over 11,000脳 energy savings in robotic pathfinding without sacrificing decision quality could revolutionize the efficiency of mobile fulfillment systems.
Hierarchical control in humanoid robots can solve complex leg-critical tasks, but performance is fragile and heavily reliant on the choice of motion tracker.
Forget finetuning: this training-free method achieves state-of-the-art zero-shot 3D visual grounding, even in messy, real-world environments.
Ditch VAEs and AMP: SLMP learns structured motion priors in a spherical latent space, enabling stable random sampling of diverse and valid humanoid behaviors without information loss.
Generate superhuman humanoid motion data at scale by closing the loop between policy performance and data difficulty, surpassing limitations of fixed datasets.
