PDRVLA: A Vision-Language-Action Model with Physical Dynamics Representations

PDRVLA introduces a lightweight Physical Dynamics Module that extracts physical tokens from vision-language features to condition action generation, without requiring online future-video generation. The module learns by directly predicting future RGB, depth, and optical-flow features, with auxiliary supervision from a frozen pretrained video world model. Training uses only public data, expanded through human-to-robot synthesis, subtask annotation, and embodied chain-of-thought (ECoT) annotation.