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Action-conditioned world models may be failing in fundamental ways, with systematic degradation in simulator fidelity revealed by a new diagnostic framework.
$\omega$-0 enables humanoid robots to seamlessly integrate movement and manipulation, outperforming traditional models by predicting coordinated actions directly from sensory inputs.
The data pyramid framework reveals how the interplay of diverse data sources can unlock new capabilities in embodied agents, highlighting critical gaps in current methodologies.
Achieving high perceptual quality in video compression at bitrates below 0.005 bpp could redefine the limits of efficient video transmission.
MLLMs stumble badly when asked to reason about safety in lab settings, dropping 32% in performance compared to general knowledge, revealing a critical gap for real-world deployment.
By decoupling camera and manipulation actions and training them in a coordinated manner, SaPaVe achieves significantly higher success rates in real-world robotic manipulation tasks compared to existing end-to-end vision-language-action models.
