scholarly journals Learning Human–Robot Interaction for Robot-Assisted Pedestrian Flow Optimization

2019 ◽  
Vol 49 (4) ◽  
pp. 797-813 ◽  
Author(s):  
Chao Jiang ◽  
Zhen Ni ◽  
Yi Guo ◽  
Haibo He
Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 119 ◽  
Author(s):  
Konstantinos Tsiakas ◽  
Maria Kyrarini ◽  
Vangelis Karkaletsis ◽  
Fillia Makedon ◽  
Oliver Korn

In this article, we present a taxonomy in Robot-Assisted Training; a growing body of research in Human–Robot Interaction which focuses on how robotic agents and devices can be used to enhance user’s performance during a cognitive or physical training task. Robot-Assisted Training systems have been successfully deployed to enhance the effects of a training session in various contexts, i.e., rehabilitation systems, educational environments, vocational settings, etc. The proposed taxonomy suggests a set of categories and parameters that can be used to characterize such systems, considering the current research trends and needs for the design, development and evaluation of Robot-Assisted Training systems. To this end, we review recent works and applications in Robot-Assisted Training systems, as well as related taxonomies in Human–Robot Interaction. The goal is to identify and discuss open challenges, highlighting the different aspects of a Robot-Assisted Training system, considering both robot perception and behavior control.


PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0140626 ◽  
Author(s):  
Kristel Knaepen ◽  
Andreas Mierau ◽  
Eva Swinnen ◽  
Helio Fernandez Tellez ◽  
Marc Michielsen ◽  
...  

2019 ◽  
Author(s):  
Francesca Ciardo ◽  
Frederike Beyer ◽  
Davide De Tommaso ◽  
Agnieszka Wykowska

In the presence of others, sense of agency (SoA), i.e. the perceived relationship between our own actions and external events, is reduced. The present study aimed at investigating whether the phenomenon of reduced SoA is observed in human-robot interaction, similarly to human-human interaction. To this end, we tested SoA when people interacted with a robot (Experiment 1), with a passive, non-agentic air pump (Experiment 2), or when they interacted with both a robot and a human being (Experiment 3). Participants were asked to rate the perceived control they felt on the outcome of their action while performing a diffusion of responsibility task. Results showed that the intentional agency attributed to the artificial entity differently affect the performance and the perceived SoA on the outcome of the task. Experiment 1 showed that, when participants successfully performed an action, they rated SoA over the outcome as lower in trials in which the robot was also able to act (but did not), compared to when they were performing the task alone. However, this did not occur in Experiment 2, where the artificial entity was an air pump, which had the same influence on the task as the robot, but in a passive manner and thus lacked intentional agency. Results of Experiment 3 showed that SoA was reduced similarly for the human and robot agents, threby indicating that attribution of intentional agency plays a crucial role in reduction of SoA. Together, our results suggest that interacting with robotic agents affects SoA, similarly to interacting with other humans, but differently from interacting with non-agentic mechanical devices. This has important implications for the applied of social robotics, where a subjective decrease in SoA could have negative consequences, such as in robot-assisted care in hospitals.


Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4088
Author(s):  
Aleš Vysocký ◽  
Stefan Grushko ◽  
Petr Oščádal ◽  
Tomáš Kot ◽  
Ján Babjak ◽  
...  

In this analysis, we present results from measurements performed to determine the stability of a hand tracking system and the accuracy of the detected palm and finger’s position. Measurements were performed for the evaluation of the sensor for an application in an industrial robot-assisted assembly scenario. Human–robot interaction is a relevant topic in collaborative robotics. Intuitive and straightforward control tools for robot navigation and program flow control are essential for effective utilisation in production scenarios without unnecessary slowdowns caused by the operator. For the hand tracking and gesture-based control, it is necessary to know the sensor’s accuracy. For gesture recognition with a moving target, the sensor must provide stable tracking results. This paper evaluates the sensor’s real-world performance by measuring the localisation deviations of the hand being tracked as it moves in the workspace.


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