人机协作绘画中，机器人实时调整姿态，降低操作者疲劳。
Adaptive Human-Robot Collaborative Painting Combining Preference-Based Optimization and Dynamic Motion Primitives
- 用偏好优化与动态运动基元实现人机协同
- 实测显著减少操作者体力消耗，提升任务效果
- 适合需要精细动作配合的协作场景
本文提出一种以人为中心的协作框架，结合基于偏好的优化（PBO）与动态运动基元（DMPs），用于优化机器人辅助绘画等任务。系统允许操作者主导过程，同时机器人实时调整工件姿态以匹配操作者手部朝向。PBO框架采用GLISp算法，通过人机反馈迭代优化执行时间、机器人响应速度和旋转放大系数等控制参数。此外，对DMP进行了改进，增强机器人反应能力与人体工程学适应性。在异质参与者群体上验证了该方法，结果表明该策略能有效降低操作者努力程度，同时优化工艺成果。
原文摘要 · Abstract (English)
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.
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