arXiv:2607.01938v1 Announce Type: cross Abstract: 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 int
The continuous development in embodied AI and robotics necessitates more robust world models that can handle dynamic physical interactions, a current frontier in AI research.
This development is crucial for advancing embodied AI, including humanoid robots, enabling them to perform complex manipulation tasks in unpredictable real-world environments with greater precision and safety.
The ability of AI systems to forecast physical dynamics accurately and in real-time, moving beyond static models to interpret and interact with dynamic 3D environments.
- · Robotics companies
- · Embodied AI developers
- · Logistics and manufacturing sectors
- · Defence industry
- · Manual labor in dangerous environments
- · AI world models lacking physics principles
Improved performance and reliability of robotic systems in manipulation tasks.
Accelerated deployment of advanced automation in industries requiring dexterity and dynamic response.
Enhanced AI capabilities contribute to more sophisticated autonomous systems across various applications, including defence and space exploration.
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