Differentiable Physic

Differentiable physics integrates physics-based simulations directly into machine learning pipelines, enabling gradient-based optimization of complex physical systems. Current research focuses on applying this approach to robotics, particularly for tasks involving contact, deformable objects, and multi-agent coordination, often employing neural networks within differentiable simulators to learn control policies or reconstruct scenes. This methodology offers significant advantages in sample efficiency and the ability to learn complex behaviors from limited data, impacting fields like robotics, computer vision, and computational physics through improved model accuracy and control.