Learning the Contact Manifold for Accurate Pose Estimation During Peg-in-Hole Insertion of Complex Geometries
Journal:
arXiv
Published Date:
May 25, 2025
Abstract
Contact-rich assembly of complex, non-convex parts with tight tolerances
remains a formidable challenge. Purely model-based methods struggle with
discontinuous contact dynamics, while model-free methods require vast data and
often lack precision. In this work, we introduce a hybrid framework that uses
only contact-state information between a complex peg and its mating hole to
recover the full SE(3) pose during assembly. In under 10 seconds of online
execution, a sequence of primitive probing motions constructs a local contact
submanifold, which is then aligned to a precomputed offline contact manifold to
yield sub-mm and sub-degree pose estimates. To eliminate costly k-NN searches,
we train a lightweight network that projects sparse contact observations onto
the contact manifold and is 95x faster and 18% more accurate. Our method,
evaluated on three industrially relevant geometries with clearances of 0.1-1.0
mm, achieves a success rate of 93.3%, a 4.1x improvement compared to
primitive-only strategies without state estimation.
