{"ID":2838334,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.18153","arxiv_id":"2511.18153","title":"A Coordinated Dual-Arm Framework for Delicate Snap-Fit Assemblies","abstract":"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.","short_abstract":"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 Snap...","url_abs":"https://arxiv.org/abs/2511.18153","url_pdf":"https://arxiv.org/pdf/2511.18153v1","authors":"[\"Shreyas Kumar\",\"Barat S\",\"Debojit Das\",\"Yug Desai\",\"Siddhi Jain\",\"Rajesh Kumar\",\"Harish J. Palanthandalam-Madapusi\"]","published":"2025-11-22T18:50:35Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.LG\"]","methods":"[]","has_code":false}