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

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.