Abstract
Ensuring that robot navigation is safe and socially acceptable is crucial for comfortable human-robot interaction in shared environments. However, existing validation methods often rely on a bird's-eye (allocentric) perspective, which fails to capture the subjective first-person experience of pedestrians encountering robots in the real world. In this paper, we address the perceptual gap between allocentric validation and egocentric experience by investigating how different perspectives affect the perceived sociability and disturbance of robot trajectories. Our approach uses an immersive VR environment to evaluate identical robot trajectories across allocentric, egocentric-proximal, and egocentric-distal viewpoints in a user study. We perform this analysis for trajectories generated from two different navigation policies to understand if the observed differences are unique to a single type of trajectory or more generalizable. We further examine whether augmenting a trajectory with a head-nod gesture can bridge the perceptual gap and improve human comfort. Our experiments suggest that trajectories rated as sociable from an allocentric view may be perceived as significantly more disturbing when experienced from a first-person perspective in close proximity. Our results also demonstrate that while passing distance affects perceived disturbance, communicative social signaling, such as a head-nod, can effectively enhance the perceived sociability of the robot's behavior.
Abstract (translated)
确保机器人在共享环境中的导航安全且符合社交规范，对于实现舒适的人机交互至关重要。然而，现有验证方法通常依赖鸟瞰（全视角）视角，无法捕捉行人在现实世界中遭遇机器人时的第一人称主观体验。本文通过研究不同视角如何影响对机器人轨迹的社交性与干扰性感知，旨在弥合全视角验证与自我视角体验之间的感知差距。我们的方法利用沉浸式虚拟现实环境，在用户研究中从全视角、自我视角-近距和自我视角-远距三个视角评估相同的机器人轨迹。我们针对两种不同导航策略生成的轨迹进行分析，以理解观察到的差异是否仅针对单一轨迹类型，还是具有更广泛的普遍性。我们还进一步检验了为轨迹添加点头手势是否能够弥合感知差距并提升人类舒适度。实验表明，在全视角下被评为具有社交性的轨迹，当以第一人称近距离体验时可能被视为显著更具干扰性。结果同时显示，虽然通行距离会影响感知干扰程度，但点头等交际性社交信号能有效提升机器人行为的感知社交性。
URL
https://arxiv.org/abs/2603.28272
