PLMD applies a denoising diffusion model to predict labels for unknown map regions, allowing goal localization in unexplored environments by substituting completed labels into existing navigation pipelines.
Designing a better asymmetric vqgan for stablediffusion
2 Pith papers cite this work. Polarity classification is still indexing.
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Sharing one discrete codebook between visual-dynamics and robot-motion branches and using the codes as auxiliary supervision improves cross-embodiment VLA success rates in the authors' real-robot tests.
citing papers explorer
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Plug-and-Play Label Map Diffusion for Universal Goal-Oriented Navigation
PLMD applies a denoising diffusion model to predict labels for unknown map regions, allowing goal localization in unexplored environments by substituting completed labels into existing navigation pipelines.
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XR-1: Towards Versatile Vision-Language-Action Models via Learning Unified Vision-Motion Representations
Sharing one discrete codebook between visual-dynamics and robot-motion branches and using the codes as auxiliary supervision improves cross-embodiment VLA success rates in the authors' real-robot tests.