Towards Coordinated Dual-Arm Snap-Fit Assembly Skill for Delicate Applications

Delicate snap-fit assemblies, such as those in precision fits like inserting a lens into an eyewear frame or in electronics, demand timely engagement detection and rapid force attenuation to prevent overshoot-induced component damage or assembly failure. In this work, we introduce a bimanual manipulation framework that integrates both capabilities into a unified skill for snap-fit assembly. The system relies solely on joint-level proprioception: a learned model detects engagement from joint-velocity transients and subsequently triggers a task-aware stiffness modulation along the insertion axis. The bimanual policy is structured around a coupled dynamical system (DS) that coordinates synchronized transport motion with selective decoupling during insertion. We evaluate the framework across varied geometries and robot platforms, demonstrating its applicability to real-world snap-fit tasks. Project video: https://shr-eyas.github.io/SNAP/