发表机构
University of Michigan at Ann Arbor（密歇根大学安娜堡分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过两项用户研究，探讨社交机器人走廊导航中意图表示与人类注意力对可读运动的影响，发现交互级意图表示能提升协调性，且部分效果在分心时仍存。
AI 中文摘要
我们关注社交导航场景中可读的机器人运动生成。在人机交互（HRI）中，可读性通常被描述为机器人运动的属性，它使观察者能够自信地推断机器人的意图。虽然已有成熟的框架用于在静态观察者面前生成可读运动，但社交机器人导航提出了新的挑战：机器人必须在动态行人环境中清晰传达其意图，同时确保人类安全，而在此类环境中，人类的注意力常常是分散的。为了实现机器人在动态和受限空间中生成可读运动的目标，我们研究了表示方式的选择和人类注意力水平如何影响导航性能和人类印象。聚焦于普遍且要求高的走廊导航场景，我们进行了两项受控用户研究，涉及在共享模型预测控制框架内实现的不同可读性公式。研究1（N=45）调查了意图表示的作用，表明通过侧可读性（特别是自适应更新时）能带来更平滑的人类运动，并且被认为比基于目的地和不可读的基线更具能力，且心理和身体负担更小。研究2（N=45）考察了行人注意力的影响，证明即使在分心情况下，可读运动也能实现平滑的人类运动，尽管这一点并未在主观评分中持续反映。综合这些发现，表明在社交机器人导航中，有效的可读运动得益于支持协调的交互级意图表示，且某些效果即使在人类注意力分散时也能持续存在。代码可在该https URL获取。
英文摘要
We focus on legible robot motion generation in social navigation settings. Legibility in human-robot interaction (HRI) is often described as the property of robot motion that enables an observer to confidently infer the robot's intent. While mature frameworks exist for generating legible motion in front of static observers, social robot navigation presents a new challenge: the robot must clearly convey its intent while ensuring human safety in dynamic pedestrian environments where human attention is often divided. With the goal of enabling robots to generate legible motion in dynamic and constrained spaces, we investigate how the choice of representation and the level of human attention shape navigation performance and human impressions. Focusing on the ubiquitous and demanding scenario of hallway navigation, we conduct two controlled user studies involving alternative legibility formulations implemented within a shared model predictive control framework. Study 1 (N = 45) investigates the role of intent representation, showing that passing-side legibility, particularly when adaptively updated, leads to smoother human motion and is perceived as more competent and less mentally and physically demanding than destination-based and non-legible baselines. Study 2 (N = 45) examines the effect of pedestrian attention, demonstrating that legible motion allows for smooth human motion even under distraction, even if this is not consistently reflected in subjective ratings. Together, these findings suggest that effective legible motion in social robot navigation benefits from interaction-level intent representations that support coordination, with some effects persisting even when human attention is divided. Code is available at https://github.com/fluentrobotics/Legible_MPPI.
Comments24 pages, 6 figures
