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

2025-06-11

AI Summary

To address insufficient trajectory adaptability in industrial human–robot collaborative carrying—caused by inter-individual variations in height and motion preferences—this paper proposes a personalized robot motion generation method integrating Dynamic Movement Primitives (DMPs) with online velocity self-adaptation scaling. It establishes, for the first time, a bidirectional closed-loop adaptation between DMP-based trajectory generation and real-time human physiological signals (EEG/EDA) as well as subjective feedback. Evaluated in an engine nacelle lip handling task, the proposed approach significantly improves user preference over the BiTRRT baseline (p < 0.01), reduces cognitive load by 23%, and enhances motion naturalness by 31%. Results demonstrate its superior real-time performance, personalization capability, and human-factor compatibility, advancing adaptive human–robot co-manipulation in industrial settings.

Technology Category

• Intelligent Robots: Manipulation
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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. Comparative analysis between DMP-generated trajectories and a state-of-the-art 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.

Problem

• Personalizing human-robot transport via Dynamic Movement Primitives
• Adapting robot velocity based on human feedback
• Enhancing interaction experience with physiological measurements

Innovation

• Dynamic Movement Primitives for trajectory generation
• Real-time velocity scaling via human feedback
• Physiological measurements for interaction evaluation