The paper introduces ActionPiece, a method for autoregressive vision-language-action models that improves action tokenization by preserving physical action relationships through joint supervision of representation learning and quantization. It uses physical rank consistency (PRC) to measure how well tokenization maintains local physical distance rankings after reconstruction, and shows improvements in policy success on multiple benchmarks.
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This method improves action tokenization by preserving physical relationships, which can lead to more accurate and context-aware action execution in vision-language-action models.
PhysBrain 1.5 is a unified model for understanding physical environments, generating actions, and predicting future states. It uses a vision-language foundation, encodes language responses and motion as sequences, and is pre-trained on human interaction videos. The model achieves strong performance on embodied understanding benchmarks.
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