scholarly journals Drawing parallels in human–other interactions: a trans-disciplinary approach to developing human–robot interaction methodologies

2019 ◽  
Vol 374 (1771) ◽  
pp. 20180433 ◽  
Author(s):  
Emily C. Collins
Keyword(s):  
Social Cognition ◽  
Attachment Styles ◽  
Empirical Work ◽  
Human Robot Interaction ◽  
Proof Of Concept ◽  
Robot Interaction ◽  
Methodology Development ◽  
Psychological Measures ◽  
Object Interaction ◽  
Human Object

This opinion paper discusses how human–robot interaction (HRI) methodologies can be robustly developed by drawing on insights from fields outside of HRI that explore human–other interactions. The paper presents a framework that draws parallels between HRIs, and human–human, human–animal and human–object interaction literature, by considering the morphology and use of a robot to aid the development of robust HRI methodologies. The paper then briefly presents some novel empirical work as proof of concept to exemplify how the framework can help researchers define the mechanism of effect taking place within specific HRIs. The empirical work draws on known mechanisms of effect in animal-assisted therapy, and behavioural observations of touch patterns and their relation to individual differences in caring and attachment styles, and details how this trans-disciplinary approach to HRI methodology development was used to explore how an interaction with an animal-like robot was impacting a user. In doing so, this opinion piece outlines how useful objective, psychological measures of social cognition can be for deepening our understanding of HRI, and developing richer HRI methodologies, which take us away from questions that simply ask ‘Is this a good robot?’, and closer towards questions that ask ‘What mechanism of effect is occurring here, through which effective HRI is being performed?’ This paper further proposes that in using trans-disciplinary methodologies, experimental HRI can also be used to study human social cognition in and of itself. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

scholarly journals How Much Information Does a Robot Need? Exploring the Benefits of Increased Sensory Range in a Simulated Crowd Navigation Task

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 112
Author(s):  
Marit Hagens ◽  
Serge Thill
Keyword(s):  
Task Performance ◽  
Human Robot Interaction ◽  
The Other ◽  
Proof Of Concept ◽  
Robot Interaction ◽  
Navigation Task ◽  
Navigation Strategy ◽  
Hardware Designs ◽  
Navigation Strategies ◽  
And Task

Perfect information about an environment allows a robot to plan its actions optimally, but often requires significant investments into sensors and possibly infrastructure. In applications relevant to human–robot interaction, the environment is by definition dynamic and events close to the robot may be more relevant than distal ones. This suggests a non-trivial relationship between sensory sophistication on one hand, and task performance on the other. In this paper, we investigate this relationship in a simulated crowd navigation task. We use three different environments with unique characteristics that a crowd navigating robot might encounter and explore how the robot’s sensor range correlates with performance in the navigation task. We find diminishing returns of increased range in our particular case, suggesting that task performance and sensory sophistication might follow non-trivial relationships and that increased sophistication on the sensor side does not necessarily equal a corresponding increase in performance. Although this result is a simple proof of concept, it illustrates the benefit of exploring the consequences of different hardware designs—rather than merely algorithmic choices—in simulation first. We also find surprisingly good performance in the navigation task, including a low number of collisions with simulated human agents, using a relatively simple A*/NavMesh-based navigation strategy, which suggests that navigation strategies for robots in crowds need not always be sophisticated.

scholarly journals Social Cognition in the Age of Human–Robot Interaction

2020 ◽  
Vol 43 (6) ◽  
pp. 373-384 ◽  
Author(s):  
Anna Henschel ◽  
Ruud Hortensius ◽  
Emily S. Cross
Keyword(s):  
Social Cognition ◽  
Human Robot Interaction ◽  
Robot Interaction

scholarly journals Use and usability of software verification methods to detect behaviour interference when teaching an assistive home companion robot: A proof-of-concept study

2021 ◽  
Vol 12 (1) ◽  
pp. 402-422
Author(s):  
Kheng Lee Koay ◽  
Matt Webster ◽  
Clare Dixon ◽  
Paul Gainer ◽  
Dag Syrdal ◽  
...  
Keyword(s):  
Software Verification ◽  
User Study ◽  
Human Robot Interaction ◽  
Proof Of Concept ◽  
Robot Interaction ◽  
Teaching System ◽  
Assistive Robots ◽  
Verification Methods ◽  
Smart Home Environment ◽  
Formal Behaviour

Abstract When studying the use of assistive robots in home environments, and especially how such robots can be personalised to meet the needs of the resident, key concerns are issues related to behaviour verification, behaviour interference and safety. Here, personalisation refers to the teaching of new robot behaviours by both technical and non-technical end users. In this article, we consider the issue of behaviour interference caused by situations where newly taught robot behaviours may affect or be affected by existing behaviours and thus, those behaviours will not or might not ever be executed. We focus in particular on how such situations can be detected and presented to the user. We describe the human–robot behaviour teaching system that we developed as well as the formal behaviour checking methods used. The online use of behaviour checking is demonstrated, based on static analysis of behaviours during the operation of the robot, and evaluated in a user study. We conducted a proof-of-concept human–robot interaction study with an autonomous, multi-purpose robot operating within a smart home environment. Twenty participants individually taught the robot behaviours according to instructions they were given, some of which caused interference with other behaviours. A mechanism for detecting behaviour interference provided feedback to participants and suggestions on how to resolve those conflicts. We assessed the participants’ views on detected interference as reported by the behaviour teaching system. Results indicate that interference warnings given to participants during teaching provoked an understanding of the issue. We did not find a significant influence of participants’ technical background. These results highlight a promising path towards verification and validation of assistive home companion robots that allow end-user personalisation.

Of Mental States and Machine Learning

2016 ◽  
Vol 60 (1) ◽  
pp. 1362-1366 ◽  
Author(s):  
Andrew Best ◽  
Samantha F. Warta ◽  
Katelynn A. Kapalo ◽  
Stephen M. Fiore
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Cognition ◽  
Mental State ◽  
Response Times ◽  
Interdisciplinary Approach ◽  
Mental States ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Machine Learning Classifiers

Using research in social cognition as a foundation, we studied rapid versus reflective mental state attributions and the degree to which machine learning classifiers can be trained to make such judgments. We observed differences in response times between conditions, but did not find significant differences in the accuracy of mental state attributions. We additionally demonstrate how to train machine classifiers to identify mental states. We discuss advantages of using an interdisciplinary approach to understand and improve human-robot interaction and to further the development of social cognition in artificial intelligence.

scholarly journals Precuneus Brain Response Changes Differently During Human-Robot and Human-Human Dyadic Social Interaction

2021 ◽  
Author(s):  
Nicolas Spatola ◽  
Thierry Chaminade
Keyword(s):  
Social Cognition ◽  
Cognitive Processes ◽  
Social Engagement ◽  
Brain Activity ◽  
Posterior Cingulate Cortex ◽  
Human Robot Interaction ◽  
Brain Response ◽  
Robot Interaction ◽  
The Social ◽  
Crucial Parameter

Abstract Human-human and human-robot interaction are often compared with the overarching question of the differences in terms of cognitive processes engaged and what can explain these differences. However, research addressing this topic, especially in neuro-imagery, use extremely artificial interaction settings. Also, they neglect a crucial parameter of human social cognition: interaction is an adaptive (rather than fixed) process. Building upon the first fMRI paradigm requiring participants to interact online with both a human and a robot in a dyadic setting, we investigate the differences and changes of brain activity during the two type of interactions in a whole brain analysis. Our results show that, grounding on a common default level, the activity in specific neural regions associated with social cognition (e.g. Posterior Cingulate Cortex) increase in HHI while remaining stable in HRI. We discuss these results regarding the iterative process of deepening the social engagement facing humans but not robots.

scholarly journals Social robotics and the modulation of social perception and bias

2019 ◽  
Vol 374 (1771) ◽  
pp. 20180037 ◽  
Author(s):  
Joshua Skewes ◽  
David M. Amodio ◽  
Johanna Seibt
Keyword(s):  
Social Cognition ◽  
Impression Formation ◽  
Psychological Research ◽  
Social Robotics ◽  
Social Robots ◽  
Human Robot Interaction ◽  
Mediated Communication ◽  
Robot Interaction ◽  
The Social ◽  
Identity Based

The field of social robotics offers an unprecedented opportunity to probe the process of impression formation and the effects of identity-based stereotypes (e.g. about gender or race) on social judgements and interactions. We present the concept of fair proxy communication—a form of robot-mediated communication that proceeds in the absence of potentially biasing identity cues—and describe how this application of social robotics may be used to illuminate implicit bias in social cognition and inform novel interventions to reduce bias. We discuss key questions and challenges for the use of robots in research on the social cognition of bias and offer some practical recommendations. We conclude by discussing boundary conditions of this new form of interaction and by raising some ethical concerns about the inclusion of social robots in psychological research and interventions. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

scholarly journals Socializing Sensorimotor Contingencies

2021 ◽  
Vol 15 ◽  
Author(s):  
Annika Lübbert ◽  
Florian Göschl ◽  
Hanna Krause ◽  
Till R. Schneider ◽  
Alexander Maye ◽  
...  
Keyword(s):  
Social Cognition ◽  
Social Contexts ◽  
Human Robot Interaction ◽  
Coordination Dynamics ◽  
Robot Interaction ◽  
Cognitive Representations ◽  
Intuitive Interfaces ◽  
Sensorimotor Coupling ◽  
Sensorimotor Contingencies ◽  
High Level

The aim of this review is to highlight the idea of grounding social cognition in sensorimotor interactions shared across agents. We discuss an action-oriented account that emerges from a broader interpretation of the concept of sensorimotor contingencies. We suggest that dynamic informational and sensorimotor coupling across agents can mediate the deployment of action-effect contingencies in social contexts. We propose this concept of socializing sensorimotor contingencies (socSMCs) as a shared framework of analysis for processes within and across brains and bodies, and their physical and social environments. In doing so, we integrate insights from different fields, including neuroscience, psychology, and research on human–robot interaction. We review studies on dynamic embodied interaction and highlight empirical findings that suggest an important role of sensorimotor and informational entrainment in social contexts. Furthermore, we discuss links to closely related concepts, such as enactivism, models of coordination dynamics and others, and clarify differences to approaches that focus on mentalizing and high-level cognitive representations. Moreover, we consider conceptual implications of rethinking cognition as social sensorimotor coupling. The insight that social cognitive phenomena like joint attention, mutual trust or empathy rely heavily on the informational and sensorimotor coupling between agents may provide novel remedies for people with disturbed social cognition and for situations of disturbed social interaction. Furthermore, our proposal has potential applications in the field of human–robot interaction where socSMCs principles might lead to more natural and intuitive interfaces for human users.

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