The Impact of Personalisation on Human-Robot Interaction in Learning Scenarios
Proceedings of the Fifth International Conference on Human Agent Interaction,
pages 171--180,
doi: 10.1145/3125739.3125756
- Oct 2017
Associated documents :
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.
@InProceedings{CABFHMMNNNSSGHNSTWW17,
author = {Churamani, Nikhil and Anton, Paul and Brügger, Marc and Fliesswasser, Erik and Hummel, Thomas and Mayer, Julius and Mustafa, Waleed and Ng, Hwei Geok and Nguyen, Thi Linh Chi and Nguyen, Quan and Soll, Marcus and Springenberg, Sebastian and Griffiths, Sascha and Heinrich, Stefan and Navarro-Guerrero, Nicolás and Strahl, Erik and Twiefel, Johannes and Weber, Cornelius and Wermter, Stefan},
title = {The Impact of Personalisation on Human-Robot Interaction in Learning Scenarios},
booktitle = {Proceedings of the Fifth International Conference on Human Agent Interaction},
editors = {},
number = {},
volume = {},
pages = {171--180},
year = {2017},
month = {Oct},
publisher = {ACM},
doi = {10.1145/3125739.3125756},
}