Abstract: Non-prehensile manipulation, such as pushing and poking, involves moving objects without grasping, offering cost-effective solutions in constrained environments. However, it presents challenges due to sensitivity to complex physics like friction and restitution. Existing approaches either rely on expensive expert demonstrations, limiting scalability, or use simulation-based trial-and-error, suffering from gaps between simulation and reality. We propose PIN-WM, a Physics-INformed World Model that efficiently identifies physical parameters for rigid bodies from visual observations, serving as an interactive environment for deployable policy learning. PIN-WM leverages differentiable physics and rendering to achieve system identification with minimal task-agnostic interactions. Built on 3D simulation, it comprehensively considers properties including mass, friction, restitution, and moment of inertia, supporting vision-based learning of 6DOF manipulation skills. To bridge gaps between the identified model and the target domain, we introduce Identified Digital Cousins, which perturbs physics and rendering parameters to generate diverse, meaningful variations for enhancing policy transfer. Evaluations across diverse task scenarios demonstrate that our method achieves zero-shot policy transfer with an impressive task success rate, significantly outperforming recent Real2Sim2Real state-of-the-arts.