A Coordinated Dual-Arm Framework for Delicate Snap-Fit Assemblies
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914168177688576 |
|---|---|
| author | Kumar, Shreyas S, Barat Das, Debojit Desai, Yug Jain, Siddhi Kumar, Rajesh Palanthandalam-Madapusi, Harish J. |
| author_facet | Kumar, Shreyas S, Barat Das, Debojit Desai, Yug Jain, Siddhi Kumar, Rajesh Palanthandalam-Madapusi, Harish J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Coordinated Dual-Arm Framework for Delicate Snap-Fit Assemblies Kumar, Shreyas S, Barat Das, Debojit Desai, Yug Jain, Siddhi Kumar, Rajesh Palanthandalam-Madapusi, Harish J. Robotics Machine Learning 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. |
| title | A Coordinated Dual-Arm Framework for Delicate Snap-Fit Assemblies |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2511.18153 |