Contact Model

Contact models in robotics aim to accurately simulate and predict the forces and interactions between robots and their environment during contact events. Current research focuses on developing more robust and efficient models, employing techniques like neural networks (including variational autoencoders and denoising diffusion models) to learn contact parameters from data, and exploring convex approximations of complex contact dynamics to improve simulation accuracy and speed. These advancements are crucial for improving the reliability and performance of robotic systems in tasks involving manipulation, assembly, and locomotion, bridging the gap between simulation and real-world performance.