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 How context and design shape human-robot trust and attributions

2020 ◽  
Vol 12 (1) ◽  
pp. 74-86
Author(s):  
Hannah Biermann ◽  
Philipp Brauner ◽  
Martina Ziefle
Keyword(s):  
Risk Perceptions ◽  
Human Robot Interaction ◽  
Complex Nature ◽  
Robot Interaction ◽  
Trust In Automation ◽  
Key Factor ◽  
Usage Context ◽  
The Impact ◽  
Design Factors ◽  
Human Robot Collaboration

AbstractIn increasingly digitized working and living environments, human-robot collaboration is growing fast with human trust toward robotic collaboration as a key factor for the innovative teamwork to succeed. This article explores the impact of design factors of the robotic interface (anthropomorphic vs functional) and usage context (production vs care) on human–robot trust and attributions. The results of a scenario-based survey with N=228 participants showed a higher willingness to collaborate with production robots compared to care. Context and design influenced the trust attributed to the robots: robots with a technical appearance in production were trusted more than anthropomorphic robots or robots in the care context. The evaluation of attributions by means of a semantic differential showed that differences in robot design were less pronounced for the production context in comparison to the care context. In the latter, anthropomorphic robots were associated with positive attributes. The results contribute to a better understanding of the complex nature of trust in automation and can be used to identify and shape use case-specific risk perceptions as well as perceived opportunities to interacting with collaborative robots. Findings of this study are pertinent to research (e.g., experts in human–robot interaction) and industry, with special regard given to the technical development and design.

scholarly journals A Taxonomy in Robot-Assisted Training: Current Trends, Needs and Challenges

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 119 ◽  
Author(s):  
Konstantinos Tsiakas ◽  
Maria Kyrarini ◽  
Vangelis Karkaletsis ◽  
Fillia Makedon ◽  
Oliver Korn
Keyword(s):  
Training Session ◽  
Training System ◽  
Human Robot Interaction ◽  
Design Development ◽  
Robot Assisted ◽  
Robot Interaction ◽  
Training Systems ◽  
Robot Perception ◽  
Educational Environments ◽  
And Behavior

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.

Using Virtual Reality to Test Human-Robot Interaction During a Collaborative Task

2019 ◽  
Author(s):  
Roberta Etzi ◽  
Siyuan Huang ◽  
Giulia Wally Scurati ◽  
Shilei Lyu ◽  
Francesco Ferrise ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Manufacturing Industry ◽  
Physiological Responses ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Assembly Task ◽  
Head Mounted Display ◽  
Collaborative Platform ◽  
Simple Assembly ◽  
Human Robot Collaboration

Abstract The use of collaborative robots in the manufacturing industry has widely spread in the last decade. In order to be efficient, the human-robot collaboration needs to be properly designed by also taking into account the operator’s psychophysiological reactions. Virtual Reality can be used as a tool to simulate human-robot collaboration in a safe and cheap way. Here, we present a virtual collaborative platform in which the human operator and a simulated robot coordinate their actions to accomplish a simple assembly task. In this study, the robot moved slowly or more quickly in order to assess the effect of its velocity on the human’s responses. Ten participants tested this application by using an Oculus Rift head-mounted display; ARTracking cameras and a Kinect system were used to track the operator’s right arm movements and hand gestures respectively. Performance, user experience, and physiological responses were recorded. The results showed that while humans’ performances and evaluations varied as a function of the robot’s velocity, no differences were found in the physiological responses. Taken together, these data highlight the relevance of the kinematic aspects of robot’s motion within a human-robot collaboration and provide valuable insights to further develop our virtual human-machine interactive platform.

scholarly journals A variable admittance control strategy for stable physical human–robot interaction

2019 ◽  
Vol 38 (6) ◽  
pp. 747-765 ◽  
Author(s):  
Federica Ferraguti ◽  
Chiara Talignani Landi ◽  
Lorenzo Sabattini ◽  
Marcello Bonfè ◽  
Cesare Fantuzzi ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Admittance Control ◽  
Robot Interaction ◽  
Interaction Control ◽  
Human Arm ◽  
Industrial Operations ◽  
Novel Method ◽  
The Stability ◽  
Stability Issues ◽  
Human Robot Collaboration

Admittance control allows a desired dynamic behavior to be reproduced on a non-backdrivable manipulator and it has been widely used for interaction control and, in particular, for human–robot collaboration. Nevertheless, stability problems arise when the environment (e.g. the human) the robot is interacting with becomes too stiff. In this paper, we investigate the stability issues related to a change of stiffness of the human arm during the interaction with an admittance-controlled robot. We propose a novel method for detecting the rise of instability and a passivity-preserving strategy for restoring a stable behavior. The results of the paper are validated on two robotic setups and with 50 users performing two tasks that emulate industrial operations.

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 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.

scholarly journals V2SOM: A Novel Safety Mechanism Dedicated to a Cobot’s Rotary Joints

Robotics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 18 ◽  
Author(s):  
Younsse Ayoubi ◽  
Med Laribi ◽  
Said Zeghloul ◽  
Marc Arsicault
Keyword(s):  
Good Control ◽  
Variable Stiffness ◽  
Human Robot Interaction ◽  
Industrial Robots ◽  
Necessary Condition ◽  
Physical Interaction ◽  
Robot Interaction ◽  
Internal Damage ◽  
Low Stiffness ◽  
The Impact

Unlike “classical” industrial robots, collaborative robots, known as cobots, implement a compliant behavior. Cobots ensure a safe force control in a physical interaction scenario within unknown environments. In this paper, we propose to make serial robots intrinsically compliant to guarantee safe physical human–robot interaction (pHRI), via our novel designed device called V2SOM, which stands for Variable Stiffness Safety-Oriented Mechanism. As its name indicates, V2SOM aims at making physical human–robot interaction safe, thanks to its two basic functioning modes—high stiffness mode and low stiffness mode. The first mode is employed for normal operational routines. In contrast, the low stiffness mode is suitable for the safe absorption of any potential blunt shock with a human. The transition between the two modes is continuous to maintain a good control of the V2SOM-based cobot in the case of a fast collision. V2SOM presents a high inertia decoupling capacity which is a necessary condition for safe pHRI without compromising the robot’s dynamic performances. Two safety criteria of pHRI were considered for performance evaluations, namely, the impact force (ImpF) criterion and the head injury criterion (HIC) for, respectively, the external and internal damage evaluation during blunt shocks.

Industrielle Mensch-Roboter-Interaktion in KMU/Industrial human-robot-collaboration in SMEs – SMEs underestimate the potential of human-robot-collaboration

2020 ◽  
Vol 110 (03) ◽  
pp. 146-150
Author(s):  
Marco Baumgartner ◽  
Tobias Kopp ◽  
Steffen Kinkel
Keyword(s):  
Human Robot Interaction ◽  
Specific Production ◽  
Robot Interaction ◽  
Collaborative Robots ◽  
Empirical Survey ◽  
Industrial Companies ◽  
Human Robot Collaboration ◽  
Production Conditions

Die industrielle Mensch-Roboter-Interaktion (MRI) eignet sich nach Einschätzung von Experten vor allem für die spezifischen Produktionsbedingungen kleiner und mittlerer Unternehmen (KMU). Nichtsdestotrotz finden sich MRI-Lösungen derzeit vorwiegend in Großunternehmen. Eine empirische Befragung von 81 Vertretern deutscher Industrieunternehmen legt die Vermutung nahe, dass es sich hierbei nicht nur um ein Umsetzungsdefizit handelt. Vielmehr scheinen KMU die Potenziale von MRI-Lösungen systematisch zu unterschätzen.   According to experts, industrial human-robot interaction (HRI) is particularly suitable for the specific production conditions of small and medium-sized enterprises (SMEs). Nevertheless, HRI solutions are currently mainly found in large companies. An empirical survey of 81 representatives of German industrial companies suggests that this is not just due to barriers in implementing collaborative robots. On the contrary, SMEs seem to systematically underestimate the potential of HRI solutions.

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