Machine Learning with Differentiable Physics Priors

Abstract

Differentiable physics priors enable gradient-based learning systems to adhere to physical dynamics. By making physics simulations differentiable, we can backpropagate through the physical consequences of actions. This pipeline allows agents to quickly learn to achieve desired effects in the physical world and is an effective technique for solving inverse problems in physical or dynamical systems. This new programming paradigm bridges model-based and data-driven methods, mitigating data scarcity and model bias simultaneously. My research focuses on developing scalable, powerful, and efficient differentiable physics simulators. We have created state-of-the-art differentiable physics for rigid bodies, cloth, fluids, articulated bodies, and deformable solids, achieving performance orders of magnitude better than existing alternatives. These differentiable simulators are applied to solve inverse problems, train control policies, and enhance reinforcement learning algorithms.