Contact-Aware Imitation Learning Through Contact Factorization

This research proposes FACE, a framework for imitation learning that enables robots to adapt to varying contact conditions while maintaining intended task behavior. By separating task behavior from environment-dependent contact factors, FACE uses normalized contact-relative coordinates and learned estimators for contact normal and friction scale, allowing for robust generalization across unseen surface properties and geometries.