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DLAM achieves superior temporal consistency and policy performance by modeling transitions as distributional latent actions, fundamentally changing how we approach action generation in VLA tasks.
Achieving state-of-the-art performance in mobile manipulation hinges on aligning temporal granularity and action space, revealing critical insights into effective world-action modeling.
By learning to project actions onto a low-dimensional manifold, ABot-M0 achieves faster and more stable robotic control policies compared to directly predicting actions in the full high-dimensional space.
