# A Coordinated Dual-Arm Framework for Delicate Snap-Fit Assemblies

arXiv: https://arxiv.org/abs/2511.18153 · HTML: https://mlsanity.com/paper/2511.18153/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Builds on (most cited first, 7 of 7)

- [InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulation](https://mlsanity.com/paper/2409.07914/) (2024)
- [Variable impedance control and learning -- A review](https://mlsanity.com/paper/2010.06246/) (2020)
- [Learning Variable Impedance Control for Contact Sensitive Tasks](https://mlsanity.com/paper/1907.07500/) (2019)
- [Learning Variable Impedance Control via Inverse Reinforcement Learning for Force-Related Tasks](https://mlsanity.com/paper/2102.06838/) (2021)
- [Learning Bimanual Manipulation via Action Chunking and Inter-Arm Coordination with Transformers](https://mlsanity.com/paper/2503.13916/) (2025)
- [BiRP: Learning Robot Generalized Bimanual Coordination using Relative Parameterization Method on Human Demonstration](https://mlsanity.com/paper/2307.05933/) (2023)
- [Dynamic Triggering Mechanisms for Event-Triggered Control](https://mlsanity.com/paper/1301.2182/) (2013)

## Abstract (arXiv)

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.