Vision--Language--Action (VLA) models generate robot actions from visual observations and language instructions, but their understanding of physical dynamics remains limited. Video world models can ca
PDRVLA improves robot action generation by integrating a lightweight physical dynamics module that predicts future visual features with world-model supervision, avoiding online video generation latency.
Vision--Language--Action (VLA) models generate robot actions from visual observations and language instructions, but their understanding of physical dynamics remains limited. Video world models can capture scene dynamics, yet generating future videos introduces high latency into the control loop. We introduce PDRVLA, whose lightweight Physical Dynamics Module 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 through a frozen pretrained video world model. Training relies entirely on publicly available…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
PDRVLA: A Vision-Language-Action Model with Physical Dynamics Representations was first seen on Hugging Face · Daily papers on Oct 28, 2026. Since then it has appeared in 23 checks on 3 different days. Its best position was #2 of 30, reached on Oct 28, 09:28 UTC. It dropped off the list after Oct 30, 03:28 UTC.
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Not at the moment. It was last on the list on Oct 30, 03:28 UTC, after 3 days of trending.
#2 on Hugging Face · Daily papers, on Oct 28, 09:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 28, 2026 and has been on the list on 3 days (23 checks) since.