发表机构
Politecnico di Milano（米兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出整合PBO与DMPs的人机协作绘画框架，通过GLISp算法优化控制参数，改进DMPs提升适应性，经实验验证可降低操作者工作量并优化绘画结果。
AI 中文摘要
本研究提出一种以人为中心的协作框架，整合基于偏好的优化（PBO）与动态运动基元（DMPs），用于优化机器人辅助绘画等任务。该系统允许操作者执行过程，机器人实时调整自身行为，动态调整工件方向以匹配操作者手部姿态。PBO框架利用GLISp算法，通过人类反馈迭代优化执行时间、机器人响应性、旋转放大等控制参数。此外，DMPs经改进以增强机器人的反应性行为及其对人体工效学要求的适应性。该方法通过一组异构参与者执行绘画任务进行验证，结果表明，所提策略可有效降低操作者工作量，同时优化过程结果。
英文摘要
This work presents a human-centered collaborative framework that integrates Preference-Based Optimization (PBO) and Dynamic Movement Primitives (DMPs) to optimize robot-assisted tasks such as painting. The system allows the operator to perform the process while the robot adapts its behavior in real-time, dynamically adjusting the orientation of the piece in order to match the orientation of the operator's hand. The PBO framework leverages the GLISp algorithm to iteratively refine control parameters such as execution time, robot responsiveness, and rotation amplification through human feedback. Moreover, DMPs have been modified to enhance the reactive behavior of the robot and its adaptability to ergonomic requirements. The method was validated with a heterogeneous group of participants executing \rev{painting tasks}. The results show that our strategy effectively reduces operator effort while optimizing process outcomes.
Comments8 pages, 8 figures
Journal refIEEE Robotics and Automation Letters, 2026
