PDRVLA：一个融合物理动态表示的视觉-语言-行动模型
PDRVLA: A Vision-Language-Action Model with Physical Dynamics Representations
PDRVLA通过集成轻量级物理动态模块改进机器人行动生成，该模块在物理世界模型监督下预测未来视觉特征，避免在线视频生成带来的延迟。PDRVLA在四个模拟基准上取得99.3%（LIBERO）、89.9%（LIBERO-Plus）、93.6%（RoboTwin~2.0）和81.5%（SimplerEnv）的成功率。
Published on Oct 27
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Abstract
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 data, expanded through human-to-robot synthesis, subtask annotation, and embodied chain-of-thought (ECoT) annotation. Across four simulation benchmarks, PDRVLA achieves success rates of 99.3% on LIBERO, 89.9% on LIBERO-Plus, 93.6% on RoboTwin~2.0, and 81.5% on SimplerEnv. Ablations show improved future prediction with world-model supervision and evaluate how subtask conditioning, future object-pose supervision, and ECoT training affect action success.
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