LaMem-VLA reconstructs robotic history into dual short-term and long-term latent memory tokens that are woven directly into a VLA model's reasoning sequence to improve long-horizon manipulation.
Global prior meets local consistency: Dual-memory augmented vision-language- action model for efficient robotic manipulation
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Dual Latent Memory in Vision-Language-Action Models for Robotic Manipulation
LaMem-VLA reconstructs robotic history into dual short-term and long-term latent memory tokens that are woven directly into a VLA model's reasoning sequence to improve long-horizon manipulation.