PearlVLA: Progressive Latent Plan Refinement for Embodied Action
Overview
PearlVLA improves action generation by refining latent plans with feedback, balancing efficiency and deliberation. It shows competitive performance on LIBERO and RoboCasa benchmarks.
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- arXiv Robotics — research abstractsPearlVLA: Progressive Latent Plan Refinement for Embodied Action
PearlVLA refines latent plans using feedback from predicted outcomes. It improves action generation while balancing latency and computational cost. Experiments show competitive performance on LIBERO and RoboCasa.
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