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178 results on '"Neerincx, Mark"'

9. Memory-Based Personalization for Fostering a Long-Term Child-Robot Relationship.

Author

Ligthart, Mike E. U., Neerincx, Mark A., and Hindriks, Koen V.

Subjects
SOCIAL robots, ROBOTS, CONTINUITY
Abstract

After the novelty effect wears off children need a new motivator to keep interacting with a social robot. Enabling children to build a relationship with the robot is the key for facilitating a sustainable long-term interaction. We designed a memory-based personalization strategy that safeguards the continuity between sessions and tailors the interaction to the child's needs and interests to foster the child-robot relationship. A longitudinal (five sessions in two months) user study (N = 46, 8-10 y.o) showed that the strategy kept children interested longer in the robot, fosters more closeness, elicits more positive social cues, and adds continuity between sessions. [ABSTRACT FROM AUTHOR]

Published
2022

18. Co-creation as a Facilitator for Co-regulation in Child-Robot Interaction.

Author

Ligthart, Mike E. U., Neerincx, Mark A., and Hindriks, Koen V.

Subjects
SOCIAL robots, ROBOT control systems, SCHOOL children, SOUNDS, SPECIAL effects in lighting
Abstract

While interacting with a social robot, children have a need to express themselves and have their expressions acknowledged by the robot. A need that is often unaddressed by the robot, due to its limitations in understanding the expressions of children. To keep the child-robot interaction manageable the robot takes control, undermining children's ability to co-regulate the interaction. Coregulation is important for having a fulfilling social interaction. We developed a co-creation activity that aims to facilitate more co-regulation. Children are enabled to create sound effects, gestures, and light shows for the robot to use during their conversation. Results from a user study (N = 59 school children, 7-11 y.o.) showed that the co-creation activity successfully facilitated co-regulation by improving children's agency. Co-creation furthermore increases children's acceptance of the robot. [ABSTRACT FROM AUTHOR]

Published
2021
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19. Inclusive HRI: Equity and Diversity in Design, Application, Methods, and Community.

Author

de Graaf, Maartje M. A., Perugia, Giulia, Fosch-Villaronga, Eduard, Lim, Angelica, Broz, Frank, Short, Elaine Schaertl, and Neerincx, Mark

Subjects
HUMAN-robot interaction, MODERN society, CIVIL rights, SCIENTIFIC community, COMMUNITIES
Abstract

Discrimination and bias are pressing issues of many AI and robotics applications. These outcomes may derive from limited datasets that do not fully represent society as a whole or from the AI scientific community's western-male configuration bias. Although being a pressing issue, understanding how robotic systems can replicate and amplify inequalities and injustice among underrepresented communities is still in its infancy among social science and technical communities. This workshop contributes to filling this gap by exploring the research question: What do diversity and inclusion mean in the context of Human-Robot Interaction (HRI)? Here, attention is directed to three different levels of HRI: the technical, the community, and the target user level. Overall, this workshop will focus on the idea that AI systems can be created to be more attuned to inclusive societal needs, respect fundamental rights, and represent contemporary values in modern societies by integrating diversity and inclusion considerations. [ABSTRACT FROM AUTHOR]

Published
2022

20. Design Patterns for an Interactive Storytelling Robot to Support Children's Engagement and Agency.

Author

Ligthart, Mike E. U., Neerincx, Mark A., and Hindriks, Koen V.

Subjects
DIGITAL storytelling, AUTONOMOUS robots, SOCIAL robots, ROBOTS, SOUNDS
Abstract

In this paper we specify and validate three interaction design patterns for an interactive storytelling experience with an autonomous social robot. The patterns enable the child to make decisions about the story by talking with the robot, reenact parts of the story together with the robot, and recording self-made sound effects. The design patterns successfully support children's engagement and agency. A user study (N = 27, 8-10 y.o.) showed that children paid more attention to the robot, enjoyed the storytelling experience more, and could recall more about the story, when the design patterns were employed by the robot during storytelling. All three aspects are important features of engagement. Children felt more autonomous during storytelling with the design patterns and highly appreciated that the design patterns allowed them to express themselves more freely. Both aspects are important features of children's agency. Important lessons we have learned are that reducing points of confusion and giving the children more time to make themselves heard by the robot will improve the patterns efficiency to support engagement and agency. Allowing children to pick and choose from a diverse set of stories and interaction settings would make the storytelling experience more inclusive for a broader range of children. [ABSTRACT FROM AUTHOR]

Published
2020
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36. Workshop on Longitudinal Human-Robot Teaming

Author

de Greeff, Joachim, primary, Hayes, Bradley, additional, Gombolay, Matthew, additional, Johnson, Matthew, additional, Neerincx, Mark, additional, van Diggelen, Jurriaan, additional, Cefkin, Melissa, additional, and Kruijff-Korbayová, Ivana, additional

Published
2018
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37. Do You Have Pain?

Author

Boumans, Roel, primary, van Meulen, Fokke, additional, Hindriks, Koen, additional, Neerincx, Mark, additional, and Olde Rikkert, Marcel, additional

Published
2018
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41. Human-Robot Co-Learning for Fluent Collaborations.

Author

van Zoelen, Emma M., van den Bosch, Karel, and Neerincx, Mark

Subjects
IMPLICIT learning, HUMAN-robot interaction
Abstract

A team develops competency by progressive mutual adaptation and learning, a process we call co-learning. In human teams, partners naturally adapt to each other and learn while collaborating. This is not self-evident in human-robot teams. There is a need for methods and models for describing and enabling co-learning in human-robot partnerships. The presented project aims to study human-robot co-learning as a process that stimulates fluent collaborations. First, it is studied how interactions develop in a context where a human and a robot both have to implicitly adapt to each other and have to learn a task to improve the collaboration and performance. The observed interaction patterns and learning outcomes will be used to (1) investigate how to design learning interactions that support human-robot teams to sustain implicitly learned behavior over time and context, and (2) to develop a mental model of the learning human partner, to investigate whether this supports the robot in its own learning as well as in adapting effectively to the human partner. [ABSTRACT FROM AUTHOR]

Published
2021
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