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

Contact Pose Identification for Peg-in-Hole Assembly under Uncertainties