Studying joint attention and hand-eye coordination in human-human interaction: A model-based approach to an automatic mapping of fixations to target objects
Studying joint attention and hand-eye coordination in human-human interaction: A model-based approach to an automatic mapping of fixations to target objects
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研究人与人交互中的联合注意力和手眼协调：基于模型的方法将注视点自动映射到目标对象
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发表时间：
2013
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通讯作者：
Thies Pfeiffer
中科院分区：
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作者：
Patrick Renner;Thies Pfeiffer
如果机器人想要在与人类共享的空间中成功地互动，它们应该学习人类在面对面互动中使用的交流信号。例如，机器人可以考虑人类的存在，通过使用个人空间的表示来抓取决策（Holthaus &Wachsmuth, 2012）。在互动过程中，对话者的眼神注视起着重要的作用。利用共同注意机制，可以在互动过程中使用凝视来确定物体，从而揭示对话者当前目标的知识（Imai et al., 2003）。众所周知，眼球运动先于手的指向或抓握（prabblanc等人，1979），这可以帮助机器人预测人类活动的区域，例如出于安全原因。我们的目标是研究交互空间中凝视和指向的模式。人类参与者的任务是在平面图上共同规划路线。为了进行分析，有必要找到特定房间和楼层以及对话者的脸或手的注视。因此，开发了一种用于自动化此映射的基于模型的方法。采用高度精确的由外向内跟踪系统作为基准，以及利用眼动仪的场景摄像头新开发的低成本的由内向外基于标记的跟踪系统，对该方法进行了评估。
If robots are to successfully interact in a space shared with humans, they should learn the communicative signals humans use in face-to-face interactions. For example, a robot can consider human presence for grasping decisions using a representation of peripersonal space (Holthaus &Wachsmuth, 2012). During interaction, the eye gaze of the interlocutor plays an important role. Using mechanisms of joint attention, gaze can be used to ground objects during interaction and knowledge about the current goals of the interlocutor are revealed (Imai et al., 2003). Eye movements are also known to precede hand pointing or grasping (Prablanc et al., 1979), which could help robots to predict areas with human activities, e.g. for security reasons. We aim to study patterns of gaze and pointing in interaction space. The human participants’ task is to jointly plan routes on a floor plan. For analysis, it is necessary to find fixations on specific rooms and floors as well as on the interlocutor’s face or hands. Therefore, a model-based approach for automating this mapping was developed. This approach was evaluated using a highly accurate outside-in tracking system as baseline and a newly developed low-cost inside-out marker-based tracking system making use of the eye tracker’s scene camera.