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DreamWAM achieves up to 75.47% accuracy in unseen scenarios, showcasing that structured future state representations can dramatically enhance action model performance beyond traditional RGB methods.
For the first time, a scaling law for quadruped motion tracking reveals that performance consistently improves with larger training datasets, unlocking new capabilities in robotic locomotion.
Moebius achieves high-fidelity image inpainting with less than 2% of the parameters of leading models, setting a new benchmark for efficiency in the field.
Autonomous vehicles can now navigate complex urban environments with significantly improved safety and smoothness thanks to a novel generator-discriminator framework that stabilizes diffusion-based motion planning.
Autonomous driving models no longer need to compromise between spatial perception and semantic reasoning: UniDriveVLA's expert decoupling unlocks state-of-the-art performance across a range of driving tasks.
