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arXiv · 2608.17691

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

Chandra Yuvesh Aubeeluck·Abilash Philip Madavath·Augustin Raju·Nicolas Pyschny·Felix Hackelöer·Florian Zwanzig
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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.

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Chandra Yuvesh Aubeeluck, Abilash Philip Madavath, Augustin Raju, Nicolas Pyschny, Felix Hackelöer, Florian Zwanzig. 2026-08-18. Force-Based Offset Estimation for Keyed Peg-in-Hole Assembly Using Local Gaussian Process Regression. https://arxiv.org/abs/2608.17691

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