发表机构
School Of Art, Design and Media, Nanyang Technological University; School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学艺术、设计与传媒学院; 南洋理工大学机械与航天工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究人机协作中机器人运动表达不确定性问题，引入数学框架，利用拉班动作分析，通过十一个描述符对五个运动原语参数化，经人体研究验证，为编码机器人运动不确定性及自主轨迹生成奠定基础。
AI 中文摘要
在人机协作中运行的机器人不仅必须传达其预期动作，还需传达因感知不完整或模糊而产生的不确定性。这项工作引入了一个通过机器人操纵器运动来表达感知不确定性的数学框架。借鉴拉班动作分析，机器人行为在一个承诺 - 警觉状态空间中组织，该空间将与不确定性相关的状态——信心、好奇心、犹豫、恐惧和不活动——映射到不同的拉班努力特征。然后使用包括加速度、暂停和后退特征、注视角度、倾斜和颤抖幅度在内的十一个运动学和几何描述符对五个运动原语——接近、暂停、后退、探索和振荡——进行参数化。基于视频的人体研究评估了对四种表达轨迹的识别以及各个描述符对感知强度的影响。参与者可靠地识别了预期行为状态，同时几个描述符显著调节了表达性。结果为在运动中编码机器人不确定性建立了基于感知的基础，并支持未来在共享环境中使用参数化运动表示进行协作任务的自主轨迹生成。
英文摘要
Robots working alongside humans must communicate their intended actions together with the uncertainty that arises from incomplete or ambiguous perception. This paper introduces a mathematical framework for expressing perceptual uncertainty through the motion of a robotic manipulator. Drawing on concepts of approach-avoidance and active perception, robot behavior is organized in a Commitment-Vigilance state space whose dimensions are represented through Laban Effort factors, mapping five uncertainty-related states, namely confidence, curiosity, hesitance, fear and inactivity on the uncertainty continuum. A kinematic analysis decomposes goal-directed end-effector motion into a radial task-progress rate and a tangential target-bearing rate, which realize the two dimensions. From this decomposition, five motion primitives, namely approach, pause, retreat, probe and twitch, are derived and parameterized using eleven kinematic descriptors covering approach and retreat characteristics, pause behavior, gaze angles, end-effector tilt and shiver amplitude. A video-based human-subject study evaluated the recognition of uncertainty-expressive trajectories and the influence of individual descriptors on perceived intensity. For every trajectory the intended behavioral state was the modal response and was selected significantly more often than chance. In single-descriptor comparisons, participants significantly preferred one variant as the more intense expression of the intended state. The results provide a perceptual basis for encoding robot uncertainty in motion and for generating such trajectories autonomously from parametric movement representations. Expressive robot motion videos and questionnaire used in the user study are available at https://anonymous.4open.science/r/aou/.
Comments14 pages, 8 figures
