Optimization Based Human-Guided Variable-Stiffness Visual Impedance Control for Contact-Rich Tasks

Abstract

In contact-rich tasks such as polishing and drilling, inevitable physical interactions often lead to task deviations due to interference, typically resulting in excessive contact forces and eventual task failure. To tackle these challenges, we propose an innovative human-guided visual-impedance control framework. Specifically, we first introduce an interaction model within image feature space, which models the dynamics of human-robot-environment interactions. Subsequently, human operation skills are characterized through human-guided wrenches, and acts on visual features through a projection matrix, thus integrating human-guided wrenches with visual-impedance interaction dynamics. Finally, leveraging this framework, we develop a novel variable-stiffness visual-impedance control strategy. The impedance parameters are optimized online via Quadratic Program, ensuring that the end-tool contact force converges to desired value while adhering to safety constraints. The validity of the proposed framework was established through polish experiments.