A Passivity-Based Framework for Dynamic Arbitration between Trajectory and Force Tracking Using Human Demonstration

Abstract

Learning from Demonstration (LfD) for contact-rich tasks faces a fundamental challenge: arbitrating between tracking a demonstrated trajectory and reproducing an interaction force. This paper introduces a novel one-shot LfD framework 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.