A Coordinated Dual-Arm Framework for Delicate Snap-Fit Assemblies
This work addresses assembly challenges in robotics for applications like electronics and eyewear, offering incremental improvements in force control and detection.
The paper tackles the problem of delicate snap-fit assemblies by introducing SnapNet for real-time engagement detection and a dual-arm coordination framework, achieving over 96% recall and up to a 30% reduction in peak impact forces.
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.