Nikhil Churamani
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.
- 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. Google Scholar
Cross Ref
- I. Elaine Allen and Christopher A. Seaman. 2007. Likert Scales and Data Analyses. Quality Progress 40, 7 (2007), 64--65.Google Scholar
- 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. Google ScholarDigital Library
- Pablo Barros and Stefan Wermter. 2016. Developing Crossmodal Expression Recognition Based on a Deep Neural Model. Adaptive Behavior 24, 5 (2016), 373--396. Google ScholarDigital Library
- 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. Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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). Google ScholarCross Ref
- Cynthia Lynn Breazeal. 2000. Sociable Machines:Expressive Social Exchange Between Humans and Robots. Dissertation. Massachusetts Institute of Technology.Google Scholar
- 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.Google Scholar
- James Dean Brown. 2011. Likert Items and Scales of Measurement? Shiken:JALT Testing & Evaluation SIG Newsletter 15, 1 (2011), 10--14.Google Scholar
- 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. Google ScholarCross Ref
- 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.Google ScholarDigital Library
- James Curran. 2017. Hotelling:Hotelling's T∧2 Test and Variants. (2017). https://cran.r-project.org/web/packages/Hotelling/index.htmlGoogle Scholar
- 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. Google ScholarCross Ref
- 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. Google ScholarCross Ref
- Bruce Frederiksen. 2008. Applying expert system technology to code reuse with Pyke. PyCon:Chicago (2008).Google Scholar
- 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. Google ScholarDigital Library
- 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).Google Scholar
- Ulf Jakobsson. 2004. Statistical Presentation and Analysis of Ordinal Data in Nursing Research. Scandinavian Journal of Caring Sciences 18, 4 (2004), 437--440. Google ScholarCross Ref
- Susan Jamieson. 2004. Likert Scales:How to (Ab)Use Them. Medical Education 38, 12 (2004), 1217--1218. Google ScholarCross Ref
- 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. Google ScholarCross Ref
- 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.Google Scholar
- 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.Google ScholarDigital Library
- 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.Google Scholar
- Rensis Likert. 1932. A Technique for the Measurement of Attitudes. Archives of Psychology 22 (1932), 55.Google Scholar
- 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.Google Scholar
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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. Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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.Google Scholar
- Ehud Reiter and Robert Dale. 1997. Building Applied Natural Language Generation Systems. Natural Language Engineering 3, 1 (1997), 57--87. Google ScholarDigital Library
- 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. Google ScholarCross Ref
- 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.Google Scholar
- 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.htmlGoogle Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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. Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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. Google ScholarCross Ref
- 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. Google ScholarCross Ref
- 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. Google ScholarCross Ref
Index Terms
- The Impact of Personalisation on Human-Robot Interaction in Learning Scenarios
Recommendations
Engaged by a Bartender Robot: Recommendation and Personalisation in Human-Robot Interaction
UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and PersonalizationBRILLO (Bartending Robot for Interactive Long-Lasting Operations) project aims to deploy an autonomous robotic bartender that can naturally interact with customers in a real-world service point. In such a scenario, this work presents a multi-modal ...
Interaction Studies with Social Robots
ICMI '15: Proceedings of the 2015 ACM on International Conference on Multimodal InteractionOver the past 10 years we have seen worldwide an immense growth of research and development into companion robots. Those are robots that fulfil particular tasks, but do so in a socially acceptable manner. The companionship aspect reflects the repeated ...
Applying politeness maxims in social robotics polite dialogue
HRI '12: Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot InteractionAn important element of human-robot interaction, as with inter-human interaction, is conversation. Having previously suggested the Gricean maxims as suitable guidelines for social robotics dialogue, we discovered that a preferable alternative set of ...
Comments