Planning interactive explanations
International Journal of Man-Machine Studies
Planning text for advisory dialogues: capturing intentional and rhetorical information
Computational Linguistics
The effect of head-nod recognition in human-robot conversation
Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction
Precision timing in human-robot interaction: coordination of head movement and utterance
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using the Rhythm of Nonverbal Human–Robot Interaction as a Signal for Learning
IEEE Transactions on Autonomous Mental Development
Editorial: Joint conferences - TAROS 2012 and FIRA 2012
Robotics and Autonomous Systems
| Hi-index | 0.00 |
Communication between socially assistive robots and humans might be facilitated by intuitively understandable mechanisms. To investigate the effects of some key nonverbal gestures on a human's own engagement and robot engagement experienced by humans, participants read a series of instructions to a robot that responded with nods, blinks, changes in gaze direction, or a combination of these. Unbeknown to the participants, the robot had no form of speech processing or gesture recognition, but simply measured speech volume levels, responding with gestures whenever a lull in sound was detected. As measured by visual analogue scales, engagement of participants was not differentially affected by the different responses of the robot. However, their perception of the robot's engagement in the task, its likability and its understanding of the instructions depended on the gesture presented, with nodding being the most effective response. Participants who self-reported greater robotics knowledge reported higher overall engagement and greater success at developing a relationship with the robot. However, self-reported robotics knowledge did not differentially affect the impact of robot gestures. This suggests that greater familiarity with robotics may help to maximise positive experiences for humans involved in human-robot interactions without affecting the impact of the type of signal sent by the robot.