PhysMani: Physics-principled 3D World Model for Dynamic Object Manipulation

Manipulating fast and dynamically moving targets in un-structured 3D environments remains challenging for embodied AI. Ex-isting visual-language-action models and world models struggle with ac-curate 3D geometry and physically meaningful forecasting. We proposePhysMani, a framework that couples a physics-principled 3D Gaussianworld model with a future-aware action policy model. The world modellearns a divergence-free Gaussian velocity field via online optimizationfor fast and physically grounded future dynamics prediction. The pol-icy model integrates the predicted 3D scene future dynamics through alearnable token based cross-attention module. We introduce PhysMani-Bench, a dynamic manipulation benchmark with 16 tasks, and demon-strate a superior success rate over strong baselines in both simulationand real-world robot experiments. Our code and data are available athttps://github.com/vLAR-group/PhysMani