Physic Engine

Physics engines are computational tools simulating physical interactions, primarily used in robotics and reinforcement learning research to create realistic training environments. Current research focuses on developing differentiable physics engines, employing techniques like variational integrators and linear complementarity problems to ensure smooth gradients for optimization and learning, and incorporating neural networks (e.g., graph neural networks) to improve generalization and handle complex materials. These advancements enable more accurate and efficient simulations, facilitating the development of robust control policies for robots and improving the design and analysis of complex physical systems.