Delicate snap-fit assemblies, such as inserting a lens into an eye-wear frame or during electronics assembly, demand timely engagement detection and rapid force attenuation to prevent overshoot-induced component damage or assembly failure. We address these challenges with two key contributions. First, we introduce SnapNet, a lightweight neural network that detects snap-fit engagement from joint-velocity transients in real-time, showing that reliable detection can be achieved using proprioceptive signals without external sensors. Second, we present a dynamical-systems-based dual-arm coordination framework that integrates SnapNet driven detection with an event-triggered impedance modulation, enabling accurate alignment and compliant insertion during delicate snap-fit assemblies. Experiments across diverse geometries on a heterogeneous bimanual platform demonstrate high detection accuracy (over 96% recall) and up to a 30% reduction in peak impact forces compared to standard impedance control.
@article{arxiv.2511.18153,
title = {A Coordinated Dual-Arm Framework for Delicate Snap-Fit Assemblies},
author = {Shreyas Kumar and Barat S and Debojit Das and Yug Desai and Siddhi Jain and Rajesh Kumar and Harish J. Palanthandalam-Madapusi},
journal= {arXiv preprint arXiv:2511.18153},
year = {2025}
}