发表机构
University of the West of England; Bristol Robotics Laboratory; Kempten University of Applied Sciences（西英格兰大学; 布里斯托机器人实验室; 肯普滕应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出LfD框架，构建含力与归一化时间维度的GMM-GMR方法，基于3142个手写演示数据集，经21人用户研究生成类人性得分71.50的轨迹，开源数据集提供基准。
AI 中文摘要
从演示中学习（LfD）提供了一种开发框架，机器人可通过观察和模仿人类动力学来发展运动技能，减少了向机器人教授技能时对显式编程的依赖。生成的类人机器人运动被认为是建立信任、实现人机自然协作的关键因素。本文提出了一种从演示中学习类人机器人运动的框架，包括数据收集、概率轨迹学习和感知用户评估。通过触摸屏遥操作界面，从22名参与者处收集了3142个手写演示的数据集，涵盖全部52种拉丁字母的大小写组合，采集了平面位置、接触力和时间信息。基于LfD中广泛使用的高斯混合模型（Gaussian Mixture Model）与高斯混合回归（Gaussian Mixture Regression）方法，本研究对该框架进行了扩展，纳入力和归一化时间维度以实现人类动力学的更丰富表示，并使其适应非连续多段轨迹，从而实现跨演示的泛化。一项由21名参与者参与的用户研究，使用介于机器人运动和类人运动之间的连续量表评估生成轨迹的感知类人性，量表归一化为0-100，其中50为中性中点。生成轨迹的总体类人性得分为71.50（标准差SD=22.56），表明大多数轨迹被感知为更具类人性。参与者指出几何定位和轨迹序列是最具影响力的感知因素，并对类人机器人行为持积极态度。该数据集作为开源资源发布，为开发和评估类人机器人运动方法提供了可复现的基准。
英文摘要
Learning from demonstration (LfD) provides a developmental framework through which robots can develop motor skills by observing and imitating human dynamics, reducing reliance on explicit programming to teach a skill to a robot. The resulting human-like robot motion is recognised as a key factor in building trust and enabling natural collaboration in human-robot interaction. This paper presents a framework for learning human-like robot motion from demonstration, including data collection, probabilistic trajectory learning, and perceptual user evaluation. A dataset of 3,142 handwriting demonstrations was collected from 22 participants across all 52 Latin alphabet character-case combinations via a touchscreen teleoperation interface, capturing planar position, contact force, and timing. Building on the widely used Gaussian Mixture Model and Gaussian Mixture Regression approach for learning from demonstration, the framework is extended in this work by incorporating force and normalised time dimensions to enable richer representation of human dynamics, and adapting it to handle non-continuous, multi-segment trajectories, enabling generalisation across demonstrations. A user study with 21 participants evaluated the perceived human-likeness of the generated trajectories using a continuous scale anchored between robotic and human-like motion, normalised to 0-100 where 50 represents the neutral midpoint. The generated trajectories achieved an overall human-likeness score of 71.50 (SD=22.56), indicating that the majority of trajectories were perceived as more human-like. Participants identified geometric positioning and trajectory sequence as the most influential perceptual factors, and reported positive attitudes toward human-like robot behaviour. The datasets are released as open-source, providing a reproducible benchmark for developing and evaluating human-like robot motion methods.
Comments9 pages, 7 figures, 4 tables, accepted for presentation at the IEEE International Conference on Development and Learning (ICDL) 2026, Kyoto, Japan, 15-18 September 2026
