Core Viewpoint - The article discusses the development of a Physics-Informed World Model (PIN-WM) that enhances the ability of robots to learn non-prehensile manipulation skills and effectively transfer these skills from simulation to real-world applications [2][4][43]. Group 1: Introduction and Background - The research team from National University of Defense Technology, Shenzhen University, and Wuhan University addresses the challenges in robot operation involving complex physical mechanisms such as friction and collision [1]. - The existing simulation environments often have significant discrepancies with real-world physics, complicating the Sim2Real transfer of robot control strategies [1]. Group 2: Methodology - PIN-WM utilizes differentiable physics and rendering to directly identify rigid body physical properties from visual observations, requiring only a small number of task-agnostic interaction trajectories for learning [3][11]. - The team introduces a Physics-Aware Digital Cousins (PADC) approach, which generates variations of the world model by perturbing identified parameters to model potential biases, thereby improving the robustness of strategy learning [3][11]. Group 3: Framework and Process - The framework consists of two main phases: system identification and strategy training, transitioning from real to simulation and back to real [10][12]. - The system identification phase involves estimating rendering and physical properties through multi-view images and interaction videos, optimizing parameters based on rendering loss [12]. Group 4: Experimental Results - The effectiveness of PIN-WM was evaluated through experiments on classic non-prehensile tasks such as "Push" and "Flip," which are sensitive to physical mechanisms [14]. - In simulation experiments, PIN-WM outperformed data-driven methods and other physical parameter identification methods, demonstrating superior generalization and performance in both "Push" and "Flip" tasks [16][17]. - Real-world experiments confirmed the advantages of PIN-WM, showing higher success rates and fewer steps required to complete tasks compared to baseline methods [17][19]. Group 5: Conclusion - The research team successfully demonstrated that PIN-WM significantly enhances the performance of non-prehensile manipulation skills in transferring from simulation to real-world scenarios, marking a notable advancement in robotic learning [43].