scholarly journals Robots Improve Judgments on Self-generated Actions: An Intentional Binding Study

2019 ◽  
Author(s):  
Cecilia Roselli ◽  
Francesca Ciardo ◽  
Agnieszka Wykowska
Keyword(s):  
Social Presence ◽  
Social Condition ◽  
Human Robot Interaction ◽  
Cognitive Mechanisms ◽  
Intentional Binding ◽  
Robot Interaction ◽  
The Social ◽  
Near Future ◽  
Robotic Agent ◽  
Individual Condition

In near future, robots will become a fundamental part of our daily life; therefore, it appears crucial to investigate how they can successfully interact with humans. Since several studies already pointed out that a robotic agent can influence human’s cognitive mechanisms such as decision-making and joint attention, we focus on Sense of Agency (SoA). To this aim, we employed the Intentional Binding (IB) task to implicitly assess SoA in human-robot interaction (HRI). Participants were asked to perform an IB task alone (Individual condition) or with the Cozmo robot (Social condition). In the Social condition, participants were free to decide whether they wanted to let Cozmo press. Results showed that participants performed the action significantly more often than Cozmo. Moreover, participants were more precise in reporting the occurrence of a self-made action when Cozmo was also in charge of performing the task. However, this improvement in evaluating self-performance corresponded to a reduction in SoA. In conclusion, the present study highlights the double effect of robots as social companions. Indeed, the social presence of the robot leads to a better evaluation of self-generated actions and, at the same time, to a reduction of SoA.

scholarly journals I–C–E Framework: Concepts for Group Dynamics Research in Human-Robot Interaction

2020 ◽  
Vol 12 (6) ◽  
pp. 1213-1229
Author(s):  
Anna M. H. Abrams ◽  
Astrid M. Rosenthal-von der Pütten
Keyword(s):  
Social Sciences ◽  
Group Dynamics ◽  
Theoretical Foundation ◽  
Human Robot Interaction ◽  
Future Research ◽  
Robot Interaction ◽  
The Past ◽  
Social Settings ◽  
The Social ◽  
Near Future

AbstractThe research community of human-robot interaction relies on theories and phenomena from the social sciences in order to study and validate robotic developments in interaction. These studies mainly concerned one (human) on one (robot) interactions in the past. The present paper shifts the attention to groups and group dynamics and reviews relevant concepts from the social sciences: ingroup identification (I), cohesion (C) and entitativity (E). Ubiquitous robots will be part of larger social settings in the near future. A conceptual framework, the I–C–E framework, is proposed as a theoretical foundation for group (dynamics) research in HRI. Additionally, we present methods and possible measures for these relevant concepts and outline topics for future research.

scholarly journals I-C-E Framework: Concepts for Group Dynamics Research in Human-Robot Interaction

2019 ◽  
Author(s):  
Anna M. H. Abrams ◽  
Astrid Rosenthal-von der Pütten
Keyword(s):  
Social Sciences ◽  
Group Dynamics ◽  
Group Identification ◽  
Human Robot Interaction ◽  
Future Research ◽  
Robot Interaction ◽  
The Past ◽  
Social Settings ◽  
The Social ◽  
Near Future

The research community of human-robot interaction relies on theories and phenomena from the social sciences in order to study and validate robotic developments in interaction. These studies mainly concerned one (human) on one (robot) interactions in the past. The present paper shifts the attention to groups and group dynamics and reviews relevant concepts from the social sciences: in-group identification (I), cohesion (C) and entitativity (E). Ubiquitous robots will be part of larger social settings in the near future. A conceptual framework, the I-C-E framework, is proposed as a theoretical foundation for group (dynamics) research in HRI. Additionally, we present methods and possible measures for these relevant concepts and outline topics for future research.

scholarly journals ERP markers of action planning and outcome monitoring in human – robot interaction.

2021 ◽  
Author(s):  
Nina-Alisa Hinz ◽  
Francesca Ciardo ◽  
Agnieszka Wykowska
Keyword(s):  
Social Presence ◽  
Action Planning ◽  
Event Related Potentials ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Outcome Monitoring ◽  
Related Potentials ◽  
The Individual ◽  
Individual Condition ◽  
The Way

The present study aimed to examine event-related potentials (ERPs) of action planning and outcome monitoring in human-robot interaction. To this end, participants were instructed to perform costly actions (i.e. losing points) to stop a balloon from inflating and to prevent its explosion. They performed the task alone (individual condition) or with a robot (joint condition). Similar to findings from human-human interactions, results showed that action planning was affected by the presence of another agent, robot in this case. Specifically, the early readiness potential (eRP) amplitude was larger in the joint, than in the individual, condition. The presence of the robot affected also outcome perception and monitoring. Our results showed that the P1/N1 complex was suppressed in the joint, compared to the individual condition when the worst outcome was expected, suggesting that the presence of the robot affects attention allocation to negative outcomes of one’s own actions. Similarly, results also showed that larger losses elicited smaller feedback-related negativity (FRN) in the joint than in the individual condition. Taken together, our results indicate that the social presence of a robot may influence the way we plan our actions and also the way we monitor their consequences. Implications of the study for the human-robot interaction field are discussed.

Shall I trust you? From child human-robot interaction to trusting relationships

2019 ◽  
Author(s):  
Cinzia Di Dio ◽  
Federico Manzi ◽  
Giulia Peretti ◽  
Angelo Cangelosi ◽  
Paul L. Harris ◽  
...  
Keyword(s):  
Behavioral Pattern ◽  
Human Robot Interaction ◽  
School Age Children ◽  
Social Relevance ◽  
Robot Interaction ◽  
Cognitive Correlates ◽  
The Social ◽  
The Relationship

Studying trust within human-robot interaction is of great importance given the social relevance of robotic agents in a variety of contexts. We investigated the acquisition, loss and restoration of trust when preschool and school-age children played with either a human or a humanoid robot in-vivo. The relationship between trust and the quality of attachment relationships, Theory of Mind, and executive function skills was also investigated. No differences were found in children’s trust in the play-partner as a function of agency (human or robot). Nevertheless, 3-years-olds showed a trend toward trusting the human more than the robot, while 7-years-olds displayed the reverse behavioral pattern, thus highlighting the developing interplay between affective and cognitive correlates of trust.

scholarly journals Neuro-cognitive mechanisms of decision making in joint action: A human–robot interaction study

2011 ◽  
Vol 30 (5) ◽  
pp. 846-868 ◽  
Author(s):  
Estela Bicho ◽  
Wolfram Erlhagen ◽  
Luis Louro ◽  
Eliana Costa e Silva
Keyword(s):  
Decision Making ◽  
Joint Action ◽  
Human Robot Interaction ◽  
Interaction Study ◽  
Cognitive Mechanisms ◽  
Robot Interaction

scholarly journals Enactivism and Robotic Language Acquisition: A Report from the Frontier

Philosophies ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 11 ◽  
Author(s):  
Frank Förster
Keyword(s):  
Language Acquisition ◽  
Scientific Investigation ◽  
Human Robot Interaction ◽  
Sense Making ◽  
Robot Interaction ◽  
Design Decisions ◽  
Potential Value ◽  
The Social ◽  
Architecture And Design

In this article, I assess an existing language acquisition architecture, which was deployed in linguistically unconstrained human–robot interaction, together with experimental design decisions with regard to their enactivist credentials. Despite initial scepticism with respect to enactivism’s applicability to the social domain, the introduction of the notion of participatory sense-making in the more recent enactive literature extends the framework’s reach to encompass this domain. With some exceptions, both our architecture and form of experimentation appear to be largely compatible with enactivist tenets. I analyse the architecture and design decisions along the five enactivist core themes of autonomy, embodiment, emergence, sense-making, and experience, and discuss the role of affect due to its central role within our acquisition experiments. In conclusion, I join some enactivists in demanding that interaction is taken seriously as an irreducible and independent subject of scientific investigation, and go further by hypothesising its potential value to machine learning.

How many words can my robot learn?

2007 ◽  
Vol 8 (1) ◽  
pp. 53-81 ◽  
Author(s):  
Luís Seabra Lopes ◽  
Aneesh Chauhan
Keyword(s):  
Word Learning ◽  
Real World ◽  
Scale Up ◽  
Human Robot Interaction ◽  
Learning System ◽  
Evaluation Methodology ◽  
Robot Interaction ◽  
Instructor Feedback ◽  
Discriminatory Capacity ◽  
Robotic Agent

This paper addresses word learning for human–robot interaction. The focus is on making a robotic agent aware of its surroundings, by having it learn the names of the objects it can find. The human user, acting as instructor, can help the robotic agent ground the words used to refer to those objects. A lifelong learning system, based on one-class learning, was developed (OCLL). This system is incremental and evolves with the presentation of any new word, which acts as a class to the robot, relying on instructor feedback. A novel experimental evaluation methodology, that takes into account the open-ended nature of word learning, is proposed and applied. This methodology is based on the realization that a robot’s vocabulary will be limited by its discriminatory capacity which, in turn, depends on its sensors and perceptual capabilities. The results indicate that the robot’s representations are capable of incrementally evolving by correcting class descriptions, based on instructor feedback to classification results. In successive experiments, it was possible for the robot to learn between 6 and 12 names of real-world office objects. Although these results are comparable to those obtained by other authors, there is a need to scale-up. The limitations of the method are discussed and potential directions for improvement are pointed out.

scholarly journals Long-term evaluation of a social robot in real homes

2016 ◽  
Vol 17 (3) ◽  
pp. 461-490 ◽  
Author(s):  
Maartje M. A. de Graaf ◽  
Somaya Ben Allouch ◽  
Jan A. G. M. van Dijk
Keyword(s):  
Social Presence ◽  
Mental Image ◽  
Human Robot Interaction ◽  
Control Beliefs ◽  
Mere Exposure Effect ◽  
Robot Interaction ◽  
Exposure Effect ◽  
Term Evaluation ◽  
Interaction Research

Abstract This study aims to contribute to emerging human-robot interaction research by adding longitudinal findings to a limited number of long-term social robotics home studies. We placed 70 robots in users’ homes for a period of up to six months, and used questionnaires and interviews to collect data at six points during this period. Results indicate that users’ evaluations of the robot dropped initially, but later rose after the robot had been used for a longer period of time. This is congruent with the so-called mere-exposure effect, which shows an increasing positive evaluation of a novel stimulus once people become familiar with it. Before adoption, users focus on control beliefs showing that previous experiences with robots or other technologies allows to create a mental image of what having and using a robot in the home would entail. After adoption, users focus on utilitarian and hedonic attitudes showing that especially usefulness, social presence, enjoyment and attractiveness are important factors for long-term acceptance.

scholarly journals A Hierarchical Behavioral Dynamic Approach for Naturally Adaptive Human-Agent Pick-and-Place Interactions

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Maurice Lamb ◽  
Patrick Nalepka ◽  
Rachel W. Kallen ◽  
Tamara Lorenz ◽  
Steven J. Harrison ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Robot Interaction ◽  
Agent Interactions ◽  
Human Agent ◽  
Dinner Table ◽  
Pick And Place ◽  
Low Dimensional ◽  
High Level ◽  
Near Future ◽  
Simple Interaction

Interactive or collaborative pick-and-place tasks occur during all kinds of daily activities, for example, when two or more individuals pass plates, glasses, and utensils back and forth between each other when setting a dinner table or loading a dishwasher together. In the near future, participation in these collaborative pick-and-place tasks could also include robotic assistants. However, for human-machine and human-robot interactions, interactive pick-and-place tasks present a unique set of challenges. A key challenge is that high-level task-representational algorithms and preplanned action or motor programs quickly become intractable, even for simple interaction scenarios. Here we address this challenge by introducing a bioinspired behavioral dynamic model of free-flowing cooperative pick-and-place behaviors based on low-dimensional dynamical movement primitives and nonlinear action selection functions. Further, we demonstrate that this model can be successfully implemented as an artificial agent control architecture to produce effective and robust human-like behavior during human-agent interactions. Participants were unable to explicitly detect whether they were working with an artificial (model controlled) agent or another human-coactor, further illustrating the potential effectiveness of the proposed modeling approach for developing systems of robust real/embodied human-robot interaction more generally.

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