PDCC: Peg-in-Hole Framework via Dynamic Continuous Contact Data

In robotic peg-in-hole assembly, contact dynamics provide critical information for precise control. This study introduces a novel peg-in-hole framework for a dynamic continuous contact environment using a supervised learning-based peg-hole extrinsic pose estimation model. The transformer- based model is trained on contact data sequences, generated in simulation with various shapes. With a single model, pose estimation performance approached 3.62mm and 1.68° for position XY and orientation XYZ, respectively. The proposed framework demonstrates effective peg-in-hole performance across 12 shapes with 5 degrees of freedom, outperforming conventional search methodologies in accuracy and efficiency, over 1.8 and 5.6 times better respectively. The study highlights the potential in dynamic continuous contact situations, contributing to precise assembly in dynamic environments.