  Peg-in-hole assembly is a challenging contact-rich manipulation task. There
is no general solution to identify the relative position and orientation
between the peg and the hole. In this paper, we propose a novel method to
classify the contact poses based on a sequence of contact measurements. When
the peg contacts the hole with pose uncertainties, a tilt-then-rotate strategy
is applied, and the contacts are measured as a group of patterns to encode the
contact pose. A convolutional neural network (CNN) is trained to classify the
contact poses according to the patterns. In the end, an admittance controller
guides the peg towards the error direction and finishes the peg-in-hole
assembly. Simulations and experiments are provided to show that the proposed
method can be applied to the peg-in-hole assembly of different geometries. We
also demonstrate the ability to alleviate the sim-to-real gap.
