Physics-Guided Vision-Language World Models for Agentic 4D Scene Understanding

This project develops a unified framework for physically grounded world modelling that combines video-based temporal prediction with Gaussian Splatting for photorealistic 3D representation. A Physics Vision-Language Model translates natural-language instructions into transformations that respect physical constraints, enabling interpretable and goal-directed control in dynamic scenes. By integrating perception, prediction, and action in a Vision-Language-Action loop, the research aims to advance agentic AI systems capable of transparent, physics-aware reasoning—supporting applications in robotics, simulation, and education.