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凝视对话模型:HHI 和 HRI 中的非言语交流。

The Gaze Dialogue Model: Nonverbal Communication in HHI and HRI.

出版信息

IEEE Trans Cybern. 2024 Apr;54(4):2026-2039. doi: 10.1109/TCYB.2022.3222077. Epub 2024 Mar 18.

DOI:10.1109/TCYB.2022.3222077
PMID:36446005
Abstract

When humans interact with each other, eye gaze movements have to support motor control as well as communication. On the one hand, we need to fixate the task goal to retrieve visual information required for safe and precise action-execution. On the other hand, gaze movements fulfil the purpose of communication, both for reading the intention of our interaction partners, as well as to signal our action intentions to others. We study this Gaze Dialogue between two participants working on a collaborative task involving two types of actions: 1) individual action and 2) action-in-interaction. We recorded the eye-gaze data of both participants during the interaction sessions in order to build a computational model, the Gaze Dialogue, encoding the interplay of the eye movements during the dyadic interaction. The model also captures the correlation between the different gaze fixation points and the nature of the action. This knowledge is used to infer the type of action performed by an individual. We validated the model against the recorded eye-gaze behavior of one subject, taking the eye-gaze behavior of the other subject as the input. Finally, we used the model to design a humanoid robot controller that provides interpersonal gaze coordination in human-robot interaction scenarios. During the interaction, the robot is able to: 1) adequately infer the human action from gaze cues; 2) adjust its gaze fixation according to the human eye-gaze behavior; and 3) signal nonverbal cues that correlate with the robot's own action intentions.

摘要

当人类相互作用时,眼动必须既能支持运动控制,又能支持交流。一方面,我们需要注视任务目标,以检索执行安全精确动作所需的视觉信息。另一方面,注视运动满足了交流的目的,既可以读取我们的互动伙伴的意图,也可以向其他人发出我们的动作意图。我们研究了这两个参与者在涉及两种类型的动作的协作任务中的目光对话:1)个体动作和 2)互动中的动作。我们记录了参与者在交互会话期间的眼动数据,以构建一个计算模型,即目光对话,对双人交互过程中的眼动相互作用进行编码。该模型还捕获了不同注视固定点之间的相关性和动作的性质。这些知识用于推断个体执行的动作类型。我们根据一名参与者的记录眼动行为对模型进行了验证,将另一名参与者的眼动行为作为输入。最后,我们使用该模型设计了一种类人机器人控制器,该控制器在人机交互场景中提供人际目光协调。在交互过程中,机器人能够:1)从目光线索中适当推断出人类的动作;2)根据人类的眼动行为调整其注视固定点;3)发出与机器人自身动作意图相关的非语言线索。

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