用动态运动基元和实时速度调节，让机器人更懂人的搬运习惯。
Human-robot collaborative transport personalization via Dynamic Movement Primitives and velocity scaling
- 基于人类反馈实时调整速度，用动态运动基元生成个性化轨迹。
- 实验显示该方法比现有最优规划器更适应人机协同，用户偏好度更高。
- 适合需要高度人机协作的工业场景，尤其关注用户体验的系统设计者。
当前工业界对人机协作愈发关注，尤其在共担任务场景中。这需要智能策略来规划机器人动作，兼顾任务约束与个体差异，如身高和移动偏好。本文提出一种新方法，利用动态运动基元（DMPs）生成个性化轨迹，并结合基于人类反馈的实时速度缩放机制。在工业级实验中，针对发动机整流罩唇部的协同搬运任务进行了严格测试。对比分析显示，结合速度缩放的DMP轨迹在适应性上优于最先进的运动规划器BiTRRT。主观评估表明用户明显更偏好基于DMP的交互方式。客观评价（包括脑电与皮肤电反应等生理指标）进一步验证了该方法在提升人机交互质量与用户体验方面的优势。
原文摘要 · Abstract (English)
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.
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