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Castellano, Ginevra
Publications (10 of 28) Show all publications
Alves-Oliveira, P., Sequeira, P., Melo, F. S., Castellano, G. & Paiva, A. (2019). Empathic robot for group learning: A field study. ACM Transactions on Human-Robot Interaction, 8(1), Article ID 3.
Open this publication in new window or tab >>Empathic robot for group learning: A field study
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2019 (English)In: ACM Transactions on Human-Robot Interaction, E-ISSN 2573-9522, Vol. 8, no 1, article id 3Article in journal (Refereed) Published
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:uu:diva-389879 (URN)10.1145/3300188 (DOI)000471141700003 ()
Available from: 2019-03-22 Created: 2019-07-31 Last updated: 2019-08-07Bibliographically approved
Gao, Y., Sibirtseva, E., Castellano, G. & Kragic, D. (2019). Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human–Robot Interaction. In: : . Paper presented at 2019 International Conference on Intelligent Robots and Systems, November 3 – 8, 2019, Macau.
Open this publication in new window or tab >>Fast Adaptation with Meta-Reinforcement Learning for Trust Modelling in Human–Robot Interaction
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In socially assistive robotics, an important research area is the development of adaptation techniques and their effect on human-robot interaction. We present a meta-learning based policy gradient method for addressing the problem of adaptation in human-robot interaction and also investigate its role as a mechanism for trust modelling. By building an escape room scenario in mixed reality with a robot, we test our hypothesis that bi-directional trust can be influenced by different adaptation algorithms. We found that our proposed model increased the perceived trustworthiness of the robot and influenced the dynamics of gaining human's trust. Additionally, participants evaluated that the robot perceived them as more trustworthy during the interactions with the meta-learning based adaptation compared to the previously studied statistical adaptation model.

National Category
Human Computer Interaction
Identifiers
urn:nbn:se:uu:diva-398405 (URN)
Conference
2019 International Conference on Intelligent Robots and Systems, November 3 – 8, 2019, Macau
Note

Yuan Gao and Elena Sibirtseva contributed equally to this work.

Available from: 2019-12-05 Created: 2019-12-05 Last updated: 2019-12-09Bibliographically approved
Paetzel, M. & Castellano, G. (2019). Let me get to know you better: Can interactions help to overcome uncanny feelings?. In: Proc. 7th International Conference on Human–Agent Interaction: . Paper presented at HAI 2019, October 6–10, Kyoto, Japan (pp. 59-67). New York: ACM Press
Open this publication in new window or tab >>Let me get to know you better: Can interactions help to overcome uncanny feelings?
2019 (English)In: Proc. 7th International Conference on Human–Agent Interaction, New York: ACM Press, 2019, p. 59-67Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
New York: ACM Press, 2019
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:uu:diva-398376 (URN)10.1145/3349537.3351894 (DOI)978-1-4503-6922-0 (ISBN)
Conference
HAI 2019, October 6–10, Kyoto, Japan
Available from: 2019-09-25 Created: 2019-12-05 Last updated: 2019-12-05Bibliographically approved
Jones, A. & Castellano, G. (2018). Adaptive robotic tutors that support self-regulated learning: A longer-term investigation with primary school children. International Journal of Social Robotics, 10(3), 357-370
Open this publication in new window or tab >>Adaptive robotic tutors that support self-regulated learning: A longer-term investigation with primary school children
2018 (English)In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, Vol. 10, no 3, p. 357-370Article in journal (Refereed) Published
Abstract [en]

Robots are increasingly being used to provide motivating, engaging and personalised support to learners. These robotic tutors have been able to increase student learning gain by providing personalised hints or problem selection. However, they have never been used to assist children in developing self regulated learning (SRL) skills. SRL skills allow a learner to more effectively self-assess and guide their own learning; learners that engage these skills have been shown to perform better academically. This paper explores how personalised tutoring by a robot achieved using an open learner model (OLM) promotes SRL processes and how this can impact learning and SRL skills compared to personalised domain support alone. An OLM allows the learner to view the model that the system holds about them. We present a longer-term study where participants take part in a geography-based task on a touch screen with adaptive feedback provided by the robot. In addition to domain support the robotic tutor uses an OLM to prompt the learner to monitor their developing skills, set goals, and use appropriate tools. Results show that, when a robotic tutor personalises and adaptively scaffolds SRL behaviour based upon an OLM, greater indication of SRL behaviour can be observed over the control condition where the robotic tutor only provides domain support and not SRL scaffolding.

National Category
Human Computer Interaction
Identifiers
urn:nbn:se:uu:diva-358539 (URN)10.1007/s12369-017-0458-z (DOI)000435423100006 ()
Available from: 2018-01-03 Created: 2018-08-30 Last updated: 2018-12-04Bibliographically approved
Obaid, M., Aylett, R., Barendregt, W., Basedow, C., Corrigan, L. J., Hall, L., . . . Castellano, G. (2018). Endowing a robotic tutor with empathic qualities: Design and pilot evaluation. International Journal of Humanoid Robotics, 15(6), Article ID 1850025.
Open this publication in new window or tab >>Endowing a robotic tutor with empathic qualities: Design and pilot evaluation
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2018 (English)In: International Journal of Humanoid Robotics, ISSN 0219-8436, Vol. 15, no 6, article id 1850025Article in journal (Refereed) Published
Abstract [en]

As increasingly more research efforts are geared towards creating robots that can teach and interact with children in educational contexts, it has been speculated that endowing robots with artificial empathy may facilitate learning. In this paper, we provide a background to the concept of empathy, and how it factors into learning. We then present our approach to equipping a robotic tutor with several empathic qualities, describing the technical architecture and its components, a map-reading learning scenario developed for an interactive multitouch table, as well as the pedagogical and empathic strategies devised for the robot. We also describe the results of a pilot study comparing the robotic tutor with these empathic qualities against a version of the tutor without them. The pilot study was performed with 26 school children aged 10–11 at their school. Results revealed that children in the test condition indeed rated the robot as more empathic than children in the control condition. Moreover, we explored several related measures, such as relational status and learning effect, yet no other significant differences were found. We further discuss these results and provide insights into future directions.

National Category
Human Computer Interaction
Identifiers
urn:nbn:se:uu:diva-368281 (URN)10.1142/S0219843618500251 (DOI)000455592000002 ()
Funder
EU, FP7, Seventh Framework Programme, ICT-317923Swedish Research Council, 2015-04378Swedish Foundation for Strategic Research , RIT15-0133
Available from: 2018-12-07 Created: 2018-12-03 Last updated: 2019-02-11Bibliographically approved
Jordan, P., Mubin, O., Obaid, M. & Silva, P. A. (2018). Exploring the referral and usage of science fiction in HCI literature. In: Design, User Experience, and Usability: Part II. Paper presented at DUXU 2018, July 15–20, Las Vegas, NV (pp. 19-38). Springer
Open this publication in new window or tab >>Exploring the referral and usage of science fiction in HCI literature
2018 (English)In: Design, User Experience, and Usability: Part II, Springer, 2018, p. 19-38Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science ; 10919
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:uu:diva-368267 (URN)10.1007/978-3-319-91803-7_2 (DOI)978-3-319-91802-0 (ISBN)
Conference
DUXU 2018, July 15–20, Las Vegas, NV
Available from: 2018-06-02 Created: 2018-12-03 Last updated: 2018-12-03Bibliographically approved
Jones, A., Bull, S. & Castellano, G. (2018). "I know that now, I'm going to learn this next": Promoting self-regulated learning with a robotic tutor. International Journal of Social Robotics, 10(4), 439-454
Open this publication in new window or tab >>"I know that now, I'm going to learn this next": Promoting self-regulated learning with a robotic tutor
2018 (English)In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, Vol. 10, no 4, p. 439-454Article in journal (Refereed) Published
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:uu:diva-363630 (URN)10.1007/s12369-017-0430-y (DOI)000445226600005 ()
Available from: 2017-11-23 Created: 2018-10-25 Last updated: 2018-10-31Bibliographically approved
Paetzel, M., Kennedy, J., Castellano, G. & Lehman, J. F. (2018). Incremental acquisition and reuse of multimodal affective behaviors in a conversational agent. In: Proc. 6th International Conference on Human-Agent Interaction: . Paper presented at HAI 2018, December 15–18, Southampton, UK (pp. 92-100). New York: ACM Press
Open this publication in new window or tab >>Incremental acquisition and reuse of multimodal affective behaviors in a conversational agent
2018 (English)In: Proc. 6th International Conference on Human-Agent Interaction, New York: ACM Press, 2018, p. 92-100Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
New York: ACM Press, 2018
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:uu:diva-377808 (URN)10.1145/3284432.3284469 (DOI)000457793300014 ()978-1-4503-5953-5 (ISBN)
Conference
HAI 2018, December 15–18, Southampton, UK
Available from: 2018-12-04 Created: 2019-02-27 Last updated: 2019-02-27Bibliographically approved
Gao, Y., Wallkötter, S., Obaid, M. & Castellano, G. (2018). Investigating deep learning approaches for human-robot proxemics. In: Proc. 27th International Symposium on Robot and Human Interactive Communication: . Paper presented at RO-MAN 2018, August 27–31, Nanjing, China (pp. 1093-1098). IEEE
Open this publication in new window or tab >>Investigating deep learning approaches for human-robot proxemics
2018 (English)In: Proc. 27th International Symposium on Robot and Human Interactive Communication, IEEE, 2018, p. 1093-1098Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we investigate the applicability of deep learning methods to adapt and predict comfortable human-robot proxemics. Proposing a network architecture, we experiment with three different layer configurations, obtaining three different end-to-end trainable models. Using these, we compare their predictive performances on data obtained during a human-robot interaction study. We find that our long short-term memory based model outperforms a gated recurrent unit based model and a feed-forward model. Further, we demonstrate how the created model can be used to create customized comfort zones that can help create a personalized experience for individual users.

Place, publisher, year, edition, pages
IEEE, 2018
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:uu:diva-366204 (URN)10.1109/ROMAN.2018.8525731 (DOI)000494315600172 ()978-1-5386-7981-4 (ISBN)
Conference
RO-MAN 2018, August 27–31, Nanjing, China
Funder
Swedish Foundation for Strategic Research , RIT15-0133Swedish Research Council, 2015-04378
Available from: 2018-11-17 Created: 2018-11-17 Last updated: 2019-12-10Bibliographically approved
Johal, W., Castellano, G., Tanaka, F. & Okita, S. (2018). Robots for Learning. International Journal of Social Robotics, 10(3), 293-294
Open this publication in new window or tab >>Robots for Learning
2018 (English)In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, Vol. 10, no 3, p. 293-294Article in journal, Editorial material (Other academic) Published
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:uu:diva-366833 (URN)10.1007/s12369-018-0481-8 (DOI)000435423100001 ()
Available from: 2018-06-07 Created: 2018-11-28 Last updated: 2018-12-07Bibliographically approved
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