Differentiable Collision

Differentiable collision detection aims to create mathematically smooth representations of collision events, enabling their seamless integration into gradient-based optimization algorithms for robotics and simulation. Current research focuses on developing efficient and accurate differentiable collision detection methods for various shapes (e.g., convex primitives, capsules, polygons), often employing techniques like randomized smoothing, convex optimization, or neural potential fields. This allows for more robust and efficient solutions in tasks such as motion planning, trajectory optimization, and physically-realistic simulations, improving the performance and safety of robotic systems.