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Robot reading human gaze: Why eye tracking is better than head tracking for human-robot collaboration

机译:机器人阅读人的视线:为什么人眼协作比人眼跟踪好于头部跟踪

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Robots are at the position to become our everyday companions in the near future. Still, many hurdles need to be cleared to achieve this goal. One of them is the fact that robots are still not able to perceive some important communication cues naturally used by humans, e.g. gaze. In the recent past, eye gaze in robot perception was substituted by its proxy, head orientation. Such an approach is still adopted in many applications today. In this paper we introduce performance improvements to an eye tracking system we previously developed and use it to explore if this approximation is appropriate. More precisely, we compare the impact of the use of eye- or head-based gaze estimation in a human robot interaction experiment with the iCub robot and naïve subjects. We find that the possibility to exploit the richer information carried by eye gaze has a significant impact on the interaction. As a result, our eye tracking system allows for a more efficient human-robot collaboration than a comparable head tracking approach, according to both quantitative measures and subjective evaluation by the human participants.
机译:机器人可以在不久的将来成为我们的日常伴侣。尽管如此,要实现这一目标还需要克服许多障碍。其中一个事实是,机器人仍然无法感知人类自然使用的一些重要交流线索,例如凝视。在最近的过去,机器人感知中的视线被其头部的朝向所替代。如今,这种方法仍在许多应用中采用。在本文中,我们向先前开发的眼动追踪系统介绍了性能改进,并使用它来探索这种近似是否合适。更准确地说,我们在与iCub机器人和幼稚对象进行的人类机器人交互实验中比较了使用基于眼睛或头部的凝视估计的影响。我们发现,利用视线所携带的丰富信息的可能性对交互具有重大影响。因此,根据人类参与者的定量测量和主观评估,我们的眼睛跟踪系统比可比的头部跟踪方法允许更有效的人机协作。

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