PhysMani: 動的物体操作のための物理原理に基づく3Dワールドモデル
PhysMani: Physics-principled 3D World Model for Dynamic Object Manipulation
動く物体を操作するために、物理原理に基づく3Dガウシアンワールドモデルと未来予測を組み合わせたフレームワークを提案し、シミュレーションと実機で高い成功率を達成した。
著者: Peng Yun, Shouwang Huang, Hao Li, Jinxi Li, Jianan Wang, Bo Yang
分類: cs.RO, cs.AI, cs.CL, cs.CV, cs.LG
原文アブストラクト
Manipulating fast and dynamically moving targets in unstructured 3D environments remains challenging for embodied AI. Existing visual-language-action models and world models struggle with accurate 3D geometry and physically meaningful forecasting. We propose PhysMani, a framework that couples a physics-principled 3D Gaussian world model with a future-aware action policy model. The world model learns a divergence-free Gaussian velocity field via online optimization for fast and physically grounded future dynamics prediction. The policy model integrates the predicted 3D scene future dynamics through a learnable token based cross-attention module. We introduce PhysMani-Bench, a dynamic manipulation benchmark with 16 tasks, and demonstrate a superior success rate over strong baselines in both simulation and real-world robot experiments.