Abstract
We describe a novel method for automatic detection of errors in
human-robot interactions. Our approach is to detect errors based on
the classification of head and shoulder movements of humans who
are interacting with erroneous robots. We conducted a user study
in which participants interacted with a robot that we programmed
to make two types of errors: social norm violations and technical
failures. During the interaction, we recorded the behavior of the
participants with a Kinect v1 RGB-D camera. Overall, we recorded
a data corpus of 237,998 frames at 25 frames per second; 83.48%
frames showed no error situation; 16.52% showed an error situation.
Furthermore, we computed six different feature sets to represent
the movements of the participants and temporal aspects of their
movements. Using this data we trained a rule learner, a Naive Bayes
classifier, and a k-nearest neighbor classifier and evaluated the
classifiers with 10-fold cross validation and leave-one-out cross
validation. The results of this evaluation suggest the following: (1)
The detection of an error situation works well, when the robot has
seen the human before; (2) Rule learner and k-nearest neighbor
classifiers work well for automated error detection when the robot
is interacting with a known human; (3) For unknown humans, the
Naive Bayes classifier performed the best; (4) The classification of
social norm violations does perform the worst; (5) There was no
big performance difference between using the original data and
normalized feature sets that represent the relative position of the
participants.
Originalsprache | Englisch |
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Titel | ICMI 2017 Proceedings of the 19th ACM International Conference on Multimodal Interaction |
Seiten | 181-188 |
Seitenumfang | 8 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2017 |
Veranstaltung | ICMI 2017 - 19th ACM International Conference on Multimodal Interaction - Dauer: 13 Nov. 2017 → 17 Nov. 2017 |
Konferenz
Konferenz | ICMI 2017 - 19th ACM International Conference on Multimodal Interaction |
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Zeitraum | 13/11/17 → 17/11/17 |
Research Field
- Nicht definiert
Schlagwörter
- Human-robot interaction
- error detection
- error situation