research-article

The Impact of Personalisation on Human-Robot Interaction in Learning Scenarios

HAI '17: Proceedings of the 5th International Conference on Human Agent Interaction

Pages 171 - 180

https://doi.org/10.1145/3125739.3125756

Published: 27 October 2017 Publication History

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.

References

[1]

Timo Ahonen, Abdenour Hadid, and Matti Pietikäinen. 2004. Face Recognition with Local Binary Patterns. In European Conference on Computer Vision (ECCV) (LNCS), Vol. 3021. Springer, Berlin, Heidelberg, Prague, Czech Republic, 469--481.

[2]

I. Elaine Allen and Christopher A. Seaman. 2007. Likert Scales and Data Analyses. Quality Progress 40, 7 (2007), 64--65.

[3]

Pablo Barros, German I. Parisi, Cornelius Weber, and Stefan Wermter. 2017. Emotion-Modulated Attention Improves Expression Recognition:A Deep Learning Model. Neurocomputing 253 (2017), 104--114.

Digital Library

[4]

Pablo Barros and Stefan Wermter. 2016. Developing Crossmodal Expression Recognition Based on a Deep Neural Model. Adaptive Behavior 24, 5 (2016), 373--396.

Digital Library

[5]

Christoph Bartneck, Dana Kuli´c, Elizabeth Croft, and Susana Zoghbi. 2008. Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots. International Journal of Social Robotics 1, 1 (2008), 71--81.

[6]

Peter N. Belhumeur, Joao P. Hespanha, and David J. Kriegman. 1997. Eigenfaces Vs. Fisherfaces:Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997), 711--720.

Digital Library

[7]

Jean-David Boucher, Ugo Pattacini, Amelie Lelong, Gerard Bailly, Frederic Elisei, Sascha Fagel, Peter F. Dominey, and Jocelyne Ventre-Dominey. 2012. I Reach Faster When I See You Look:Gaze Effects in Human--Human and Human--Robot Face-to-Face Cooperation. Frontiers in Neurorobotics 6, 3 (2012).

[8]

Cynthia Lynn Breazeal. 2000. Sociable Machines:Expressive Social Exchange Between Humans and Robots. Dissertation. Massachusetts Institute of Technology.

[9]

Rodney A. Brooks, Cynthia Breazeal, Matthew Marjanovi´c, Brian Scassellati, and Matthew M. Williamson. 1999. The Cog Project:Building a Humanoid Robot. In Computation for Metaphors, Analogy, and Agents. Number 1562 in LNCS. Springer Berlin Heidelberg, 52--87.

[10]

James Dean Brown. 2011. Likert Items and Scales of Measurement? Shiken:JALT Testing & Evaluation SIG Newsletter 15, 1 (2011), 10--14.

[11]

Allison Bruce, Illah Nourbakhsh, and Reid Simmons. 2002. The Role of Expressiveness and Attention in Human-Robot Interaction. In IEEE International Conference on Robotics and Automation (ICRA), Vol. 4. IEEE, Washington, DC, USA, 4138--4142.

[12]

Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research 12, Aug (2011), 2493--2537.

Digital Library

[13]

James Curran. 2017. Hotelling:Hotelling's T∧2 Test and Variants. (2017). https://cran.r-project.org/web/packages/Hotelling/index.html

[14]

Kerstin Dautenhahn. 1995. Getting to know each other -- Artificial social intelligence for autonomous robots. Robotics and Autonomous Systems 16, 2 (1995), 333 -- 356. Moving the Frontiers between Robotics and Biology.

[15]

Sander Dieleman and Benjamin Schrauwen. 2014. End-to-End Learning for Music Audio. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Florence, Italy, 6964--6968.

[16]

Bruce Frederiksen. 2008. Applying expert system technology to code reuse with Pyke. PyCon:Chicago (2008).

[17]

Manuel Giuliani, Ronald P.A. Petrick, Mary Ellen Foster, Andre Gaschler, Amy Isard, Maria Pateraki, and Markos Sigalas. 2013. Comparing Task-Based and Socially Intelligent Behaviour in a Robot Bartender. In ACM on International Conference on Multimodal Interaction (ICMI '13). ACM, Sydney, Australia, 263--270.

Digital Library

[18]

Xavier Hinaut, Johannes Twiefel, Marcelo Borghetti Soares, Pablo Barros, Luiza Mici, and Stefan Wermter. 2015. Humanoidly speaking--Learning about the world and language with a humanoid friendly robot. International Joint Conference on Artificial Intelligence Video competition (2015).

[19]

Ulf Jakobsson. 2004. Statistical Presentation and Analysis of Ordinal Data in Nursing Research. Scandinavian Journal of Caring Sciences 18, 4 (2004), 437--440.

[20]

Susan Jamieson. 2004. Likert Scales:How to (Ab)Use Them. Medical Education 38, 12 (2004), 1217--1218.

[21]

Frédéric Kaplan. 2004. Who is afraid of the Humanoid? Investigating cultural differences in the acceptance of robots. International Journal of Humanoid Robotics 01, 03 (2004), 465--480.

[22]

Matthias Kerzel, Erik Strahl, Sven Magg, Nicolás Navarro-Guerrero, Stefan Heinrich, and Stefan Wermter. 2017. NICO -- Neuro-Inspired COmpanion:A Developmental Humanoid Robot Platform for Multimodal Interaction. In IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, Lisbon, Portugal. In Press.

[23]

Rachel Kirby, Jodi Forlizzi, and Reid Simmons. 2010. Affective social robots. Robotics and Autonomous Systems 58, 3 (2010), 322 -- 332. Towards Autonomous Robotic Systems 2009:Intelligent, Autonomous Robotics in the UK.

Digital Library

[24]

Min Kyung Lee, Jodi Forlizzi, Sara Kiesler, Paul Rybski, John Antanitis, and Sarun Savetsila. 2012. Personalization in HRI:A field experiment. In 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI). 319--326.

[25]

Rensis Likert. 1932. A Technique for the Measurement of Attitudes. Archives of Psychology 22 (1932), 55.

[26]

Wenyong Lin. 2015. An Improved GMM-Based Clustering Algorithm for Efficient Speaker Identification. In International Conference on Computer Science and Network Technology (ICCSNT), Vol. 1. IEEE, Harbin, China, 1490--1493.

[27]

Edward Loper and Steven Bird. 2002. NLTK:The Natural Language Toolkit. In Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics - Volume 1 (ETMTNLP '02). Association for Computational Linguistics, Stroudsburg, PA, USA, 63--70.

Digital Library

[28]

Patrick Lucey, Jeffrey F. Cohn, Takeo Kanade, Jason Saragih, Zara Ambadar, and Iain Matthews. 2010. The Extended Cohn-Kanade Dataset (CK+):A Complete Dataset for Action Unit and Emotion-Specified Expression. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops. IEEE, San Francisco, CA USA, 94--101.

[29]

Yanick Lukic, Carlo Vogt, Oliver Dürr, and Thilo Stadelmann. 2016. Speaker Identification and Clustering Using Convolutional Neural Networks. In IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, Salerno, Italy, 1--6.

[30]

Seiichi Nakagawa, Longbiao Wang, and Shinji Ohtsuka. 2012. Speaker Identification and Verification by Combining MFCC and Phase Information. IEEE Transactions on Audio, Speech, and Language Processing 20, 4 (2012), 1085--1095.

Digital Library

[31]

Hwei Geok Ng, Paul Anton, Marc Brügger, Nikhil Churamani, Erik Fließwasser, Thomas Hummel, Julius Mayer, Waleed Mustafa, Thi Linh Chi Nguyen, Quan Nguyen, Marcus Soll, Sebastian Springenberg, Sascha Griffiths, Stefan Heinrich, Nicolás Navarro-Guerrero, Erik Strahl, Johannes Twiefel, Cornelius Weber, and Stefan Wermter. 2017. Hey Robot, Why Don't You Talk to Me?. In IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, Lisbon, Portugal. In Press.

[32]

Kathrin Pollmann. 2014. Does the Human-Like Behavior of a Robot Evoke Action Co-Representation in a Human Co-Actor? MSc Thesis. Technische Universiteit Eindhoven, Eindhoven, The Netherlands.

[33]

Ehud Reiter and Robert Dale. 1997. Building Applied Natural Language Generation Systems. Natural Language Engineering 3, 1 (1997), 57--87.

Digital Library

[34]

Joe Saunders, Dag Sverre Syrdal, Kheng Lee Koay, Nathan Burke, and Kerstin Dautenhahn. 2016. 'Teach Me - Show Me' End-User Personalization of a Smart Home and Companion Robot. IEEE Transactions on Human-Machine Systems 46, 1 (Feb 2016), 27--40.

[35]

Florian Schroff, Dmitry Kalenichenko, and James Philbin. 2015. FaceNet:A Unified Embedding for Face Recognition and Clustering. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Boston, MA, USA, 815--823.

[36]

Seija Sirkia, Jari Miettinen, Klaus Nordhausen, Hannu Oja, and Sara Taskinen. 2013. SpatialNP:Multivariate Nonparametric Methods Based on Spatial Signs and Ranks. (2013). https://cran.r-project.org/web/packages/SpatialNP/index.html

[37]

Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 Shared Task:Language-Independent Named Entity Recognition. In Conference on Natural Language Learning at HLT-NAACL (CONLL '03), Vol. 4. Association for Computational Linguistics, Edmonton, Alberta, Canada, 142--147.

[38]

Johannes Twiefel, Timo Baumann, Stefan Heinrich, and Stefan Wermter. 2014. Improving Domain-Independent Cloud-Based Speech Recognition with Domain-Dependent Phonetic Post-Processing. In AAAI Conference on Artificial Intelligence, Vol. Twenty-Eighth. AAAI Press, Québec City, Québec, Canada, 1529--1535.

[39]

Johannes Twiefel, Xavier Hinaut, Marcelo Borghetti, Erik Strahl, and Stefan Wermter. 2016. Using Natural Language Feedback in a Neuro-Inspired Integrated Multimodal Robotic Architecture. In IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, New York, NY, USA, 52--57.

[40]

Kees van Deemter, Mariët Theune, and Emiel Krahmer. 2005. Real Versus Template-Based Natural Language Generation:A False Opposition? Computational Linguistics 31, 1 (2005), 15--24.

Digital Library

[41]

Viswanath Venkatesh, Michael G. Morris, Gordon B. Davis, and Fred D. Davis. 2003. User Acceptance of Information Technology:Toward a Unified View. MIS Quarterly 27, 3 (2003), 425--478.

Digital Library

[42]

Paul Viola and Michael Jones. 2001. Rapid Object Detection Using a Boosted Cascade of Simple Features. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1. IEEE, Kauai, Hawaii, USA, 511--518.

[43]

Dorothy Watson. 1992. Correcting for Acquiescent Response Bias in the Absence of a Balanced Scale:An Application to Class Consciousness. Sociological Methods & Research 21, 1 (1992), 52--88.

[44]

Xiaojia Zhao and DeLiang Wang. 2013. Analyzing Noise Robustness of MFCC and GFCC Features in Speaker Identification. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Vancouver, BC, Canada, 7204--7208.

Cited By

Aoudni YBalasubramani MNatarajan KSabeenian RSreenivasa Rao VSesha Lakshmi P(2025)Advancing personalized human-robot interaction in the smart world through emotional AI in entertainment robotsEntertainment Computing10.1016/j.entcom.2024.10077052(100770)Online publication date: Jan-2025
https://doi.org/10.1016/j.entcom.2024.100770
Tarakli IDi Nuovo A(2025)Personalised Interactive Reinforcement Learning with Multi-task Pre-trainingHuman-Friendly Robotics 202410.1007/978-3-031-81688-8_19(255-262)Online publication date: 26-Feb-2025
https://doi.org/10.1007/978-3-031-81688-8_19
Duncan JAlambeigi FPryor M(2024)A Survey of Multimodal Perception Methods for Human–Robot Interaction in Social EnvironmentsACM Transactions on Human-Robot Interaction10.1145/365703013:4(1-50)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3657030
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The Impact of Personalisation on Human-Robot Interaction in Learning Scenarios

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Published In

cover image ACM Conferences

HAI '17: Proceedings of the 5th International Conference on Human Agent Interaction

October 2017

550 pages

ISBN:9781450351133

DOI:10.1145/3125739

General Chairs:
Britta Wrede
Bielefeld University, Germany
,
Yukie Nagai
Osaka University, Japan
,
Program Chairs:
Takanori Komatsu
Meiji University, Japan
,
Marc Hanheide
University of Lincoln, UK
,
Lorenzo Natale
Italian Institute of Technology, Italy

Copyright © 2017 ACM.

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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New York, NY, United States

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Published: 27 October 2017

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HAI '17: The Fifth International Conference on Human-Agent Interaction

October 17 - 20, 2017

Bielefeld, Germany

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Overall Acceptance Rate 121 of 404 submissions, 30%

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Cited By

Aoudni YBalasubramani MNatarajan KSabeenian RSreenivasa Rao VSesha Lakshmi P(2025)Advancing personalized human-robot interaction in the smart world through emotional AI in entertainment robotsEntertainment Computing10.1016/j.entcom.2024.10077052(100770)Online publication date: Jan-2025
https://doi.org/10.1016/j.entcom.2024.100770
Tarakli IDi Nuovo A(2025)Personalised Interactive Reinforcement Learning with Multi-task Pre-trainingHuman-Friendly Robotics 202410.1007/978-3-031-81688-8_19(255-262)Online publication date: 26-Feb-2025
https://doi.org/10.1007/978-3-031-81688-8_19
Duncan JAlambeigi FPryor M(2024)A Survey of Multimodal Perception Methods for Human–Robot Interaction in Social EnvironmentsACM Transactions on Human-Robot Interaction10.1145/365703013:4(1-50)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3657030
Mukherjee DHong JVats HBae SNajjaran H(2024)Personalization of industrial human–robot communication through domain adaptation based on user feedbackUser Modeling and User-Adapted Interaction10.1007/s11257-024-09394-134:4(1327-1367)Online publication date: 22-Mar-2024
https://doi.org/10.1007/s11257-024-09394-1
LACROIX DWullenkord REyssel FCastellano GRiek LCakmak MLeite I(2023)Who's in Charge?Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3568294.3580152(580-586)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1145/3568294.3580152
McMillan DJaber RCowan BFischer JIrfan BCumbal RZargham NLee MCastellano GRiek LCakmak MLeite I(2023)Human-Robot Conversational Interaction (HRCI)Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3568294.3579954(923-925)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1145/3568294.3579954
Maroto-Gómez MMarqués-Villaroya SCastillo JCastro-González ÁMalfaz M(2023)Active learning based on computer vision and human–robot interaction for the user profiling and behavior personalization of an autonomous social robotEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105631117(105631)Online publication date: Jan-2023
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Larsson SLiinason MTanqueray LCastellano G(2023)Towards a Socio-Legal Robotics: A Theoretical Framework on Norms and Adaptive TechnologiesInternational Journal of Social Robotics10.1007/s12369-023-01042-915:11(1755-1768)Online publication date: 20-Oct-2023
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Rossi SStaffa Mde Graaf MGena C(2023)Preface to the special issue on personalization and adaptation in human–robot interactive communicationUser Modeling and User-Adapted Interaction10.1007/s11257-023-09365-y33:2(189-194)Online publication date: 23-Apr-2023
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Onorati TCastro-González Ádel Valle JDíaz PCastillo J(2023)Creating Personalized Verbal Human-Robot Interactions Using LLM with the Robot MiniProceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023)10.1007/978-3-031-48306-6_15(148-159)Online publication date: 25-Nov-2023
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