scholarly journals Where to Next? The Impact of COVID-19 on Human-Robot Interaction Research

2020 ◽  
Vol 10 (1) ◽  
pp. 1-7 ◽  
Author(s):  
David Feil-Seifer ◽  
Kerstin S. Haring ◽  
Silvia Rossi ◽  
Alan R. Wagner ◽  
Tom Williams
Keyword(s):  
Human Robot Interaction ◽  
Robot Interaction ◽  
The Impact ◽  
Interaction Research

scholarly journals eXtended Artificial Intelligence: New Prospects of Human-AI Interaction Research

2021 ◽  
Vol 2 ◽  
Author(s):  
Carolin Wienrich ◽  
Marc Erich Latoschik
Keyword(s):  
Artificial Intelligence ◽  
Human Perception ◽  
Systematic Investigation ◽  
Gender Effect ◽  
Human Robot Interaction ◽  
Theoretical Treatment ◽  
Robot Interaction ◽  
Application Fields ◽  
The Impact ◽  
Interaction Research

Artificial Intelligence (AI) covers a broad spectrum of computational problems and use cases. Many of those implicate profound and sometimes intricate questions of how humans interact or should interact with AIs. Moreover, many users or future users do have abstract ideas of what AI is, significantly depending on the specific embodiment of AI applications. Human-centered-design approaches would suggest evaluating the impact of different embodiments on human perception of and interaction with AI. An approach that is difficult to realize due to the sheer complexity of application fields and embodiments in reality. However, here XR opens new possibilities to research human-AI interactions. The article’s contribution is twofold: First, it provides a theoretical treatment and model of human-AI interaction based on an XR-AI continuum as a framework for and a perspective of different approaches of XR-AI combinations. It motivates XR-AI combinations as a method to learn about the effects of prospective human-AI interfaces and shows why the combination of XR and AI fruitfully contributes to a valid and systematic investigation of human-AI interactions and interfaces. Second, the article provides two exemplary experiments investigating the aforementioned approach for two distinct AI-systems. The first experiment reveals an interesting gender effect in human-robot interaction, while the second experiment reveals an Eliza effect of a recommender system. Here the article introduces two paradigmatic implementations of the proposed XR testbed for human-AI interactions and interfaces and shows how a valid and systematic investigation can be conducted. In sum, the article opens new perspectives on how XR benefits human-centered AI design and development.

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 KASPAR – a minimally expressive humanoid robot for human–robot interaction research

2009 ◽  
Vol 6 (3-4) ◽  
pp. 369-397 ◽  
Author(s):  
Kerstin Dautenhahn ◽  
Chrystopher L. Nehaniv ◽  
Michael L. Walters ◽  
Ben Robins ◽  
Hatice Kose-Bagci ◽  
...  
Keyword(s):  
Humanoid Robot ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Interaction Research

Validating extended neglect tolerance model for human robot interactions in humanoid soccer robots

Robotica ◽  
2010 ◽  
Vol 29 (3) ◽  
pp. 421-432 ◽  
Author(s):  
R. E. Mohan ◽  
W. S. Wijesoma ◽  
C. A. A. Calderon ◽  
C. J. Zhou
Keyword(s):  
False Alarm ◽  
Task Complexity ◽  
Human Robot Interaction ◽  
False Alarms ◽  
Robot Interaction ◽  
Tolerance Model ◽  
Robot Performance ◽  
Robot Tasks ◽  
The Impact ◽  
Soccer Robots

SUMMARYEstimating robot performance in human robot teams is a vital problem in human robot interaction community. In a previous work, we presented extended neglect tolerance model for estimation of robot performance, where the human operator switches control between robots sequentially based on acceptable performance levels, taking into account any false alarms in human robot interactions. Task complexity is a key parameter that directly impacts the robot performance as well as the false alarms occurrences. In this paper, we validate the extended neglect tolerance model for two robot tasks of varying complexity levels. We also present the impact of task complexity on robot performance estimations and false alarms demands. Experiments were performed with real and virtual humanoid soccer robots across tele-operated and semi-autonomous modes of autonomy. Measured false alarm demand and robot performances were largely consistent with the extended neglect tolerance model predictions for both real and virtual robot experiments. Experiments also showed that the task complexity is directly proportional to false alarm demands and inversely proportional to robot performance.

scholarly journals An Immersive Investment Game to Study Human-Robot Trust

2021 ◽  
Vol 8 ◽  
Author(s):  
Sebastian Zörner ◽  
Emy Arts ◽  
Brenda Vasiljevic ◽  
Ankit Srivastava ◽  
Florian Schmalzl ◽  
...  
Keyword(s):  
Science Fiction ◽  
Positive Impact ◽  
Space Mission ◽  
Verbal Communication ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Investment Game ◽  
Robot Perception ◽  
The Impact ◽  
Human Robot Collaboration

As robots become more advanced and capable, developing trust is an important factor of human-robot interaction and cooperation. However, as multiple environmental and social factors can influence trust, it is important to develop more elaborate scenarios and methods to measure human-robot trust. A widely used measurement of trust in social science is the investment game. In this study, we propose a scaled-up, immersive, science fiction Human-Robot Interaction (HRI) scenario for intrinsic motivation on human-robot collaboration, built upon the investment game and aimed at adapting the investment game for human-robot trust. For this purpose, we utilize two Neuro-Inspired COmpanion (NICO) - robots and a projected scenery. We investigate the applicability of our space mission experiment design to measure trust and the impact of non-verbal communication. We observe a correlation of 0.43 (p=0.02) between self-assessed trust and trust measured from the game, and a positive impact of non-verbal communication on trust (p=0.0008) and robot perception for anthropomorphism (p=0.007) and animacy (p=0.00002). We conclude that our scenario is an appropriate method to measure trust in human-robot interaction and also to study how non-verbal communication influences a human’s trust in robots.

scholarly journals Joint action with iCub: a successful adaptation of a paradigm of cognitive neuroscience in HRI

2019 ◽  
Author(s):  
Jairo Pérez-Osorio ◽  
Davide De Tommaso ◽  
Ebru Baykara ◽  
Agnieszka Wykowska
Keyword(s):  
Cognitive Psychology ◽  
Human Brain ◽  
Cognitive Neuroscience ◽  
Human Robot Interaction ◽  
Human Cognition ◽  
Social Environments ◽  
Social Signals ◽  
Robot Interaction ◽  
Robot Behavior ◽  
The Impact

Robots will soon enter social environments shared with humans. We need robots that are able to efficiently convey social signals during interactions. At the same time, we need to understand the impact of robots’ behavior on the human brain. For this purpose, human behavioral and neural responses to the robot behavior should be quantified offering feedback on how to improve and adjust robot behavior. Under this premise, our approach is to use methods of experimental psychology and cognitive neuroscience to assess the human’s reception of a robot in human-robot interaction protocols. As an example of this approach, we report an adaptation of a classical paradigm of experimental cognitive psychology to a naturalistic human- robot interaction scenario. We show the feasibility of such an approach with a validation pilot study, which demonstrated that our design yielded a similar pattern of data to what has been previously observed in experiments within the area of cognitive psychology. Our approach allows for addressing specific mechanisms of human cognition that are elicited during human-robot interaction, and thereby, in a longer-term perspective, it will allow for designing robots that are well- attuned to the workings of the human brain.

A Comparative Study on the Effect of Mechanical Compliance for a Safe Physical Human–Robot Interaction

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Yu She ◽  
Siyang Song ◽  
Hai-Jun Su ◽  
Junmin Wang
Keyword(s):  
Impact Force ◽  
Variable Stiffness ◽  
Human Robot Interaction ◽  
Design Parameters ◽  
Robot Dynamics ◽  
Robot Interaction ◽  
Promising Alternative ◽  
Mechanical Compliance ◽  
Maximum Impact Force ◽  
The Impact

Abstract In this paper, we study the effects of mechanical compliance on safety in physical human–robot interaction (pHRI). More specifically, we compare the effect of joint compliance and link compliance on the impact force assuming a contact occurred between a robot and a human head. We first establish pHRI system models that are composed of robot dynamics, an impact contact model, and head dynamics. These models are validated by Simscape simulation. By comparing impact results with a robotic arm made of a compliant link (CL) and compliant joint (CJ), we conclude that the CL design produces a smaller maximum impact force given the same lateral stiffness as well as other physical and geometric parameters. Furthermore, we compare the variable stiffness joint (VSJ) with the variable stiffness link (VSL) for various actuation parameters and design parameters. While decreasing stiffness of CJs cannot effectively reduce the maximum impact force, CL design is more effective in reducing impact force by varying the link stiffness. We conclude that the CL design potentially outperforms the CJ design in addressing safety in pHRI and can be used as a promising alternative solution to address the safety constraints in pHRI.

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