Kumar, ShreyasShreyasKumarBarat, S.S.BaratDas, DebojitDebojitDasJain, SiddhiSiddhiJainKumar, RajeshRajeshKumarPalanthandalam-Madapusi, Harish J.Harish J.Palanthandalam-Madapusi2025-11-262025-11-262025-01-01[9798331598693]10.1109/Humanoids65713.2025.112031422-s2.0-105022176309http://repository.iitgn.ac.in/handle/IITG2025/33548Delicate 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/falseConference Paper21640580934-93520250