A Passivity-Based Framework for Dynamic Arbitration between Trajectory and Force Tracking using Human Demonstration
Yeoil Yun, Youngwuk Kim, Junchul Gwak, Hyungpil Moon, Hyouk Ryeol Choi, Ja Choon Koo
AI summary
Problem
Learning from demonstration for contact-rich tasks struggles to balance precise trajectory tracking with accurate force reproduction, often failing when environmental geometry deviates or when switching control modes causes instability.
Approach
The method converts the operator’s physical grip force into a continuous blending weight that smoothly transitions between impedance and admittance controllers, while a dual-layer passivity system safely manages energy injection to prevent instability.
Key results
- Grip force used as a continuous, intuitive signal for dynamic control arbitration
- Robust replication of demonstrated interaction forces under positional uncertainty
- Dual-layer passivity assurance with dynamic energy arbitration between tank and null-space dissipation
- Experimental validation on a 7-DOF manipulator showing superior safety and performance over conventional impedance control
Why it matters
Provides a practical, sensor-light method for safely transferring complex contact-rich manipulation skills to robots without intrusive physiological measurements or precise environmental modeling.
Abstract
Learning from Demonstration (LfD) for contact- rich tasks faces a fundamental challenge: arbitrating between tracking a demonstrated trajectory and reproducing an inter- action force. This paper introduces a novel one-shot LfD frame- work that resolves this conflict by leveraging the operator’s grip force as an intuitive, continuous signal for arbitration. This signal allows the controller to seamlessly transition between a trajectory-tracking impedance controller and a force-tracking admittance controller, prioritizing path accuracy when the demonstrated grip was light and interaction force fidelity when it was firm. To ensure verifiably safe interaction, the adaptive control law is integrated within a dual-layer passivity assurance framework. This mechanism intelligently distributes potentially non-passive energy between an energy tank and adaptive null-space dissipation to guarantee energetic stability. The proposed framework was experimentally validated on a 7-DOF manipulator, demonstrating that the controller autonomously reproduces interaction forces and shows significant robustness against environmental position uncertainties, a scenario where conventional impedance controllers can fail.