Learning Stable Nonlinear Dynamics and Interactive Force-Aware Variable Impedance Control for Robotic Contact Tasks

This paper presents a novel approach for efficient robot programming in tasks such as polishing and grinding, which addresses the challenges of stability and adaptability in robot motion and control. The approach involves learning movement skills and variable impedance control from human demonstrations. Firstly, a non-parametric Gaussian mixture model and globally stable method are proposed for learning robot rhythmic skills with nonlinear dynamics by taking into account the characteristics of robot motion in human-robot physical interaction. Then, adaptive variable impedance control mechanisms are constructed using Gaussian mixture regression, enabling adaptive perception and online control of unknown environments. Finally, a stability-consideration variable impedance controller is designed to minimize the total control-torque while ensuring motion accuracy, which allows efficient adaptation to uncertain environments and complex tasks. The paper provides a novel framework of robot rhythmic skill learning and variable impedance control for robotic complex surface polishing tasks with nonlinear dynamics based on multiple physical human demonstrations, and verifies the proposed methods with a real Franka-robot polishing task. These implementations would be with applications in intelligent manufacturing, robot-assisted rehabilitation.