Insert-One: One-Shot Robust Visual-Force Servoing for Novel Object Insertion with 6-DoF Tracking

Abstract

Recent advancements in autonomous robotic assembly have shown promising results, especially in addressing the precision insertion challenge. However, achieving adaptability across diverse object categories and tasks often necessitates a learning phase that requires costly real-world data collection. Moreover, previous research often assumes either the rigid attachment of the inserted object to the robot’s end-effector or relies on precise calibration within structured environments. We propose a one-shot method for high-precision contact-rich manipulation assembly tasks, enabling a robot to perform insertions of new objects from randomly presented orientations using just a single demonstration image. Our method incorporates a hybrid framework that blends 6-DoF visual tracking-based iterative control and impedance control, facilitating high-precision tasks with real-time visual feedback. Importantly, our approach requires no pre-training and demonstrates resilience against uncertainties arising from camera pose calibration errors and disturbances in the object in-hand pose. We validate the effectiveness of the proposed framework through extensive experiments in real-world scenarios, encompassing various high-precision assembly tasks.