Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | HAI 2017 - Proceedings of the 5th International Conference on Human Agent Interaction |
| Seiten | 171-180 |
| Seitenumfang | 10 |
| ISBN (elektronisch) | 9781450351133 |
| Publikationsstatus | Veröffentlicht - 17 Okt. 2017 |
| Extern publiziert | Ja |
| Veranstaltung | 5th International Conference on Human Agent Interaction, HAI 2017 - Bielefeld, Deutschland Dauer: 17 Okt. 2017 → 20 Okt. 2017 |
Abstract
Advancements in Human-Robot Interaction involve robots being more responsive and adaptive to the human user they are interacting with. For example, robots model a personalised dialogue with humans, adapting the conversation to accommodate the user's preferences in order to allow natural interactions. This study investigates the impact of such personalised interaction capabilities of a human companion robot on its social acceptance, perceived intelligence and likeability in a human-robot interaction scenario. In order to measure this impact, the study makes use of an object learning scenario where the user teaches different objects to the robot using natural language. An interaction module is built on top of the learning scenario which engages the user in a personalised conversation before teaching the robot to recognise different objects. The two systems, i.e. with and without the interaction module, are compared with respect to how different users rate the robot on its intelligence and sociability. Although the system equipped with personalised interaction capabilities is rated lower on social acceptance, it is perceived as more intelligent and likeable by the users.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Mensch-Maschine-Interaktion
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- Harvard
- Apa
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- RIS
HAI 2017 - Proceedings of the 5th International Conference on Human Agent Interaction. 2017. S. 171-180.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - The impact of personalisation on human-robot interaction in learning scenarios
AU - Churamani, Nikhil
AU - Anton, Paul
AU - Brügger, Marc
AU - Fliebwasser, Erik
AU - Hummel, Thomas
AU - Mayer, Julius
AU - Mustafa, Waleed
AU - Ng, Hwei Geok
AU - Nguyen, Thi Linh Chi
AU - Nguyen, Quan
AU - Soll, Marcus
AU - Springenberg, Sebastian
AU - Griffiths, Sascha
AU - Heinrich, Stefan
AU - Navarro-Guerrero, Nicolas
AU - Strahl, Erik
AU - Twiefel, Johannes
AU - Weber, Cornelius
AU - Wermter, Stefan
N1 - Publisher Copyright: © 2017 ACM.
PY - 2017/10/17
Y1 - 2017/10/17
N2 - Advancements in Human-Robot Interaction involve robots being more responsive and adaptive to the human user they are interacting with. For example, robots model a personalised dialogue with humans, adapting the conversation to accommodate the user's preferences in order to allow natural interactions. This study investigates the impact of such personalised interaction capabilities of a human companion robot on its social acceptance, perceived intelligence and likeability in a human-robot interaction scenario. In order to measure this impact, the study makes use of an object learning scenario where the user teaches different objects to the robot using natural language. An interaction module is built on top of the learning scenario which engages the user in a personalised conversation before teaching the robot to recognise different objects. The two systems, i.e. with and without the interaction module, are compared with respect to how different users rate the robot on its intelligence and sociability. Although the system equipped with personalised interaction capabilities is rated lower on social acceptance, it is perceived as more intelligent and likeable by the users.
AB - Advancements in Human-Robot Interaction involve robots being more responsive and adaptive to the human user they are interacting with. For example, robots model a personalised dialogue with humans, adapting the conversation to accommodate the user's preferences in order to allow natural interactions. This study investigates the impact of such personalised interaction capabilities of a human companion robot on its social acceptance, perceived intelligence and likeability in a human-robot interaction scenario. In order to measure this impact, the study makes use of an object learning scenario where the user teaches different objects to the robot using natural language. An interaction module is built on top of the learning scenario which engages the user in a personalised conversation before teaching the robot to recognise different objects. The two systems, i.e. with and without the interaction module, are compared with respect to how different users rate the robot on its intelligence and sociability. Although the system equipped with personalised interaction capabilities is rated lower on social acceptance, it is perceived as more intelligent and likeable by the users.
KW - Companion robots
KW - Dialogue management
KW - Human-robot interaction
KW - Natural languageunderstanding
KW - Person identification
KW - Person localisation
KW - Personalisation
KW - Personalised robots
KW - Social robotics
KW - Speech processing
UR - http://www.scopus.com/inward/record.url?scp=85034850580&partnerID=8YFLogxK
U2 - 10.1145/3125739.3125756
DO - 10.1145/3125739.3125756
M3 - Conference contribution
AN - SCOPUS:85034850580
SP - 171
EP - 180
BT - HAI 2017 - Proceedings of the 5th International Conference on Human Agent Interaction
T2 - 5th International Conference on Human Agent Interaction, HAI 2017
Y2 - 17 October 2017 through 20 October 2017
ER -