Force-Based Offset Estimation for Keyed Peg-in-Hole Assembly Using Local Gaussian Process Regression

Abstract:Key-keyway assembly tasks impose strict geometric constraints and are highly sensitive to grasp pose deviations in uncertain environments. This work presents a force-based offset estimation method for keyed peg-in-hole assembly, embedded within a perception-validation-insertion pipeline. Residual misalignment is estimated directly from wrist force/torque measurements using a local KNN-Gaussian Process hybrid regressor. The framework distinguishes between two contact regimes, hard collision and guided chamfer insertion, and routes inference to a dedicated model for each. Regime classification is achieved via a contact-window duration threshold. KNN combined with a deterministic search using the results of a post-grasp monocular visual validation contributes to an increased accuracy of the regressor model. This approach achieves accurate radial offset estimation in chamfered peg insertion, during a keypoint detection-based pick and place application. Experiments using the integrated force/torque sensor of a collaborative robot arm showed an increase in insertion success rate from 67% to 87% after the pipeline was applied.

6 pages, 11 figures. Accepted and presented at the 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2026), Genova, Italy. Awaiting publication in IEEE Xplore

Robotics (cs.RO)

arXiv:2608.17691 [cs.RO]

(or arXiv:2608.17691v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.17691 arXiv-issued DOI via DataCite (pending registration)