Geometry-aware manipulationExpert
Robot Learning on Discrete Surfaces: Theory and Applications
Preprint. The authors build a discrete Riemannian framework that lets robots learn motion and functions directly on polyhedral surface meshes rather than treating surfaces as external constraints. They apply it to Dynamic Movement Primitives, Gaussian Processes, and Riemannian Flow Matching, showing stability improvements over MeshDMP and roughly halving RFM training time. This matters for any contact-rich surface task such as polishing, drawing, or inspection on CAD or reconstructed meshes.