Human-robot collaborative transport personalization via Dynamic Movement Primitives and velocity scaling

Abstract

Nowadays, industries are showing a growing interest in human-robot collaboration, particularly for shared tasks. This requires intelligent strategies to plan a robot’s motions, considering both task constraints and human-specific factors such as height and movement preferences. This work introduces a novel approach to generate personalized trajectories using Dynamic Movement Primitives (DMPs), enhanced with real-time velocity scaling based on human feedback. The method was rigorously tested in industrial-grade experiments, focusing on the collaborative transport of an engine cowl lip section. A comparative analysis between DMP-generated trajectories and a standard industrial motion planner (BiTRRT) highlights their adaptability, combined with velocity scaling. Subjective user feedback further demonstrates a clear preference for DMP-based interactions. Objective evaluations, including physiological measurements from brain and skin activity, reinforce these findings, showcasing the advantages of DMPs in enhancing human-robot interaction and improving user experience.