用舞蹈语言让机器人动作传情达意，提升人机协作透明度。
Employing Laban Shape for Generating Emotionally and Functionally Expressive Trajectories in Robotic Manipulators
- 引入拉班动作符号中的形态元素，设计新型迟疑式运动轨迹。
- 实验表明两种新轨迹被用户感知为能力下降，增强情感轨迹可区分情绪维度。
- 适合关注人机交互、具身智能与非语言沟通的研究者。
人机协作的成功依赖于连贯的沟通与对机器人能力、目标及约束的精准理解。尽管机械臂具备高精度、多功能和高效率，但其动作缺乏表情与变化，隐藏了意图，导致人机协作效率低且不透明。本文采用拉班动作记谱法（Laban notation）这一舞蹈标注语言，使机械臂生成具有功能表达性的轨迹——通过非语言线索传达自身能力及任务成功率预期。我们提出两种新型迟疑式运动（尖刺状与弧线状），并借助拉班形态（Laban Shape）增强四种既有情感轨迹（快乐、悲伤、害羞、愤怒）的情感表现力。通过人类被试实验验证：两种迟疑轨迹均被感知为机器人能力降低；增强后的情感轨迹在情感空间（VAD谱）中呈现可区分的情绪特征，证实了拉班形态的有效性。
原文摘要 · Abstract (English)
Successful human-robot collaboration depends on cohesive communication and a precise understanding of the robot's abilities, goals, and constraints. While robotic manipulators offer high precision, versatility, and productivity, they exhibit expressionless and monotonous motions that conceal the robot's intention, resulting in a lack of efficiency and transparency with humans. In this work, we use Laban notation, a dance annotation language, to enable robotic manipulators to generate trajectories with functional expressivity, where the robot uses nonverbal cues to communicate its abilities and the likelihood of succeeding at its task. We achieve this by introducing two novel variants of Hesitant expressive motion (Spoke-Like and Arc-Like). We also enhance the emotional expressivity of four existing emotive trajectories (Happy, Sad, Shy, and Angry) by augmenting Laban Effort usage with Laban Shape. The functionally expressive motions are validated via a human-subjects study, where participants equate both variants of Hesitant motion with reduced robot competency. The enhanced emotive trajectories are shown to be viewed as distinct emotions using the Valence-Arousal-Dominance (VAD) spectrum, corroborating the usage of Laban Shape.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。