๐ค AI Summary
This work addresses the challenge in contact-intensive manipulation where conventional disturbance observers struggle to distinguish between system inertial response and genuine external torque, thereby limiting the sensitivity of zero-torque control. To overcome this, the paper proposes a Dynamic Wrench Disturbance Observer (DW-DOB) that explicitly embeds task-space inertia into the observerโs nominal modelโan approach introduced here for the first time. This design effectively decouples internal dynamics from external contact torques, significantly enhancing the accuracy of minute torque estimation. Grounded in passivity theory, the method ensures interaction stability under dynamic contact without relying on learning-based mechanisms. Experimental validation on an H7/h6 industrial clearance-fit peg-in-hole assembly task demonstrates superior performance: compared to traditional disturbance observers and PD control, the proposed approach achieves deeper, more compliant insertion with substantially reduced residual torque.
๐ Abstract
This paper proposes a Dynamic Wrench Disturbance Observer (DW-DOB) designed to achieve highly sensitive zero-wrench control in contact-rich manipulation. By embedding task-space inertia into the observer nominal model, DW-DOB cleanly separates intrinsic dynamic reactions from true external wrenches. This preserves sensitivity to small forces and moments while ensuring robust regulation of contact wrenches. A passivity-based analysis further demonstrates that DW-DOB guarantees stable interactions under dynamic conditions, addressing the shortcomings of conventional observers that fail to compensate for inertial effects. Peg-in-hole experiments at industrial tolerances (H7/h6) validate the approach, yielding deeper and more compliant insertions with minimal residual wrenches and outperforming a conventional wrench disturbance observer and a PD baseline. These results highlight DW-DOB as a practical learning-free solution for high-precision zero-wrench control in contact-rich tasks.