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Endowing VLMs with intrinsic 3D geometric awareness and physical interaction cues via XEmbodied substantially boosts performance on spatial reasoning and embodied tasks, surpassing existing 2D image-text pretrained models.
Stop struggling with compounding errors in long-horizon robotic tasks: AtomVLA leverages LLMs and latent world models to decompose tasks and score actions, boosting success rates to 97% on LIBERO.
