PDRVLA: A vision-language-action model with physical dynamics representations

PDRVLA proposes a lightweight Physical Dynamics Module that extracts physical tokens from vision-language features to condition action generation, without online video generation. The module learns through direct prediction of future RGB, depth, and optical-flow features, with auxiliary supervision from a frozen pretrained video world model. All training data comes from public data and was expanded through human-to-robot synthesis, subtask annotation, and embodied chain-of-thought (ECoT) annotation.