PDRVLA: A Vision-Language-Action Model with Physical Dynamics Representations
PDRVLA is a vision-language-action model that adds a lightweight Physical Dynamics Module. The module extracts physical tokens from vision-language features to condition robot action generation without online video generation. It learns by predicting future RGB, depth, and optical-flow features, with auxiliary supervision from a frozen pretrained video world model. Training uses publicly available data expanded through human-to-robot synthesis, subtask annotation, and embodied chain-of-thought annotation. Reported success rates across four simulation benchmarks are 99.3% on LIBERO, 89.9% on LIBERO-Plus, 93.6% on RoboTwin 2.0, and 81.5% on SimplerEnv.
Open original source huggingface.coOne primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
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PDRVLA is a vision-language-action model that adds a lightweight Physical Dynamics Module. The module extracts physical tokens from vision-language features to condition robot action generation without online video generation. It learns by predicting future RGB, depth, and optical-flow features, with auxiliary supervision from a frozen pretrained video world model. Training uses publicly available data expanded through human-to-robot synthesis, subtask annotation, and embodied chain-of-thought annotation. Reported success rates across four simulation benchmarks are 99.3% on LIBERO, 89.9% on LIBERO-Plus, 93.6% on RoboTwin 2.0, and 81.5% on SimplerEnv.
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Results are reported on simulation benchmarks only; real-robot performance and source claims were not independently checked.
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Key findings from the stored brief
- The Physical Dynamics Module conditions action generation on physical tokens without generating future videos online, avoiding control-loop latency.
- The module predicts future RGB, depth, and optical-flow features, supervised auxiliarily by a frozen pretrained video world model.
- Reported simulation success rates are 99.3% on LIBERO, 89.9% on LIBERO-Plus, 93.6% on RoboTwin 2.0, and 81.5% on SimplerEnv.
- Ablations report improved future prediction with world-model supervision and evaluate subtask conditioning, object-pose supervision, and ECoT training.
Results are reported on simulation benchmarks only; real-robot performance and source claims were not independently checked.
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Why this may matter
Gives robot policies a learned sense of physical dynamics while keeping inference fast enough for real-time control, using only publicly available training data.
Limits and open questions
Results are reported on simulation benchmarks only; real-robot performance and source claims were not independently checked.
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