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Hierarchical latent actions and a novel memory gating mechanism enable HiMem-WAM to excel in long-horizon robotic manipulation, outperforming traditional models in robustness and task performance.
Real-time robot control just got a 50x speed boost thanks to MotuBrain's efficient world action model.
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.
