Robot Learning on Discrete Surfaces: Theory and Applications
The work develops a unified discrete Riemannian framework to enable robot learning directly on polyhedral surface meshes by creating consistent approximations of differential geometry operators like logarithmic maps and parallel transport. This allows for robust learning in Dynamic Movement Primitives, Gaussian Processes, and Riemannian Flow Matching, overcoming limitations of existing methods that ignore the mesh's underlying geometric structure.