Harnessing Machine Learning to Model Intuitive Physics: Insights from Game Engines and Cartoon Simulations
Jiazhen Tang · Applied and Computational Engineering · 2024
This paper explores the application of machine learning (ML) in modeling intuitive physics, focusing on insights derived from game engines and cartoon simulations. Intuitive physics refers to the human capability to predict physical phenomena in everyday situations, a skill that can be replicated and enhanced in machines using ML algorithms. By integrating ML with game engines, which provide dynamic, controllable environments, and cartoon simulations, known for their exaggerated physical scenarios, the research aims to develop models that better understand and predict physical interactions. The study analyzes the efficiency of current ML approaches, including deep learning and reinforcement learning, in capturing the complex, often non-intuitive physics depicted in cartoons, and how these models can be generalized to understand real-world physics. The findings suggest potential improvements in the algorithms' ability to interpret and anticipate physical events, leading to advancements in robotics and AI-driven simulation systems.