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| Content Provider | ACM Digital Library |
|---|---|
| Author | Poller, Peter Barz, Michael Sonntag, Daniel |
| Abstract | Gaze is known to be a dominant modality for conveying spatial information, and it has been used for grounding in human-robot dialogues. In this work, we present the prototype of a gaze-supported multi-modal dialogue system that enhances two core tasks in human-robot collaboration: 1) our robot is able to learn new objects and their location from user instructions involving gaze, and 2) it can instruct the user to move objects and passively track this movement by interpreting the user's gaze. We performed a user study to investigate the impact of different eye trackers on user performance. In particular, we compare a head-worn device and an RGB-based remote eye tracker. Our results show that the head-mounted eye tracker outperforms the remote device in terms of task completion time and the required number of utterances due to its higher precision. |
| Starting Page | 79 |
| Ending Page | 80 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781450348850 |
| DOI | 10.1145/3029798.3038367 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-03-06 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Human-centred computing Human-robot interaction Machine learning Eye tracking Multi-modal interaction |
| Content Type | Text |
| Resource Type | Article |
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