A conceptual framework to evaluate human-robot collaboration

Gervasi, Riccardo; Mastrogiacomo, Luca; Franceschini, Fiorenzo

doi:10.1007/s00170-020-05363-1

Cited by 147 publications

(105 citation statements)

References 65 publications

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“…Some evaluation frameworks that consider an overall human-centered perspective, i.e., focus on the intended end-users of the robot, in the interaction between humans and robots already exist (e.g., [ 73 , 74 , 75 ]). Recently, Hoffman [ 73 ] focuses on fluency as an indicator of the quality of the interaction in human-robot collaboration (HRC).…”

Section: Introductionmentioning

confidence: 99%

“…These mechanisms may be the underlying pre-requisite for achieving mutual and subsequently ‘fluent’ interaction between humans and robots. Garvasi et al [ 74 ] have developed a general conceptual framework to evaluate the collaboration between humans and robots that encompasses many aspects of collaboration, in which human-centered usability issues are addressed. Both Hoffman [ 73 ] and Garvasi et al [ 74 ] focus on some human-centered aspects of the human operator in these present developed evaluation frameworks, but we intend to extend their human-centered approaches to a UX approach, based on the following motivations.…”

Section: Introductionmentioning

confidence: 99%

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The ANEMONE: Theoretical Foundations for UX Evaluation of Action and Intention Recognition in Human-Robot Interaction

Lindblom¹,

Alenljung²

2020

Sensors

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The coexistence of robots and humans in shared physical and social spaces is expected to increase. A key enabler of high-quality interaction is a mutual understanding of each other’s actions and intentions. In this paper, we motivate and present a systematic user experience (UX) evaluation framework of action and intention recognition between humans and robots from a UX perspective, because there is an identified lack of this kind of evaluation methodology. The evaluation framework is packaged into a methodological approach called ANEMONE (action and intention recognition in human robot interaction). ANEMONE has its foundation in cultural-historical activity theory (AT) as the theoretical lens, the seven stages of action model, and user experience (UX) evaluation methodology, which together are useful in motivating and framing the work presented in this paper. The proposed methodological approach of ANEMONE provides guidance on how to measure, assess, and evaluate the mutual recognition of actions and intentions between humans and robots for investigators of UX evaluation. The paper ends with a discussion, addresses future work, and some concluding remarks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The ANEMONE: Theoretical Foundations for UX Evaluation of Action and Intention Recognition in Human-Robot Interaction

Lindblom¹,

Alenljung²

2020

Sensors

View full text Add to dashboard Cite

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“…The direct physical interaction of humans and robots without barriers means new safety risks for employees [29]. In order to minimize these, cobots are equipped with various integrated safety features defined by ISO/TS 15066:2016 [30] allowing high levels of automation in the vicinity of the operator [31], namely safety-rated monitored stop, speed and separation monitoring, power and force limiting, and hand guiding.…”

Section: Industrial Human-robot Interactionmentioning

confidence: 99%

“…Therefore, the position of humans and the cobot must be known at any time. The implementation can be carried out using different approaches such as 3D image processing or inertial motion capture suits [29]. In contrast to the safety-rated stop, the presence of persons does not necessarily lead to a stop in operation, but initially results in a slowing of the robot's movements.…”

Section: Industrial Human-robot Interactionmentioning

confidence: 99%

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Success factors for introducing industrial human-robot interaction in practice: an empirically driven framework

Kopp

Baumgartner

Kinkel

2020

Int J Adv Manuf Technol

114

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Human-robot interaction (HRI) promises to be a means whereby manufacturing companies will be able to address current challenges like a higher demand for customization. However, despite comparably low costs, there are only few applications in practice. To date, it remains unclear which factors facilitate or hinder the successful introduction of industrial collaborative robots (cobots). In a three-step approach, we first developed a comprehensive two-dimensional framework covering three separate phases and four essential components for human-robot working systems. Secondly, we reviewed related literature to identify relevant success factors. Thirdly, in an online survey we asked leading representatives of German manufacturing companies (n = 81) to assess the importance of these factors from a practical point of view. The results reveal that besides technology-related factors like occupational safety and appropriate cobot configuration, employee-centered factors like the fear of job loss and ensuring an appropriate level of trust in the robot are considered important. However, company representatives seem to underestimate the impact of subtle measures to increase employee acceptance which could be incorporated into internal communication strategies prior to and during the introduction of cobots. Comparative analysis based on three distinct application scenarios suggests that most success factors’ practical importance is independent of the motivation for implementing HRI. Furthermore, answers from practitioners in free-text fields reveal that success factors which intuitively come to their mind such as financial factors are not necessarily perceived most important. Finally, we argue for more application-oriented research that focuses on practically relevant factors to guide HRI research, inform cobot development, and support companies in overcoming apparent barriers.

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Utilization of Deep Learning Models for Safe Human‐Friendly Computing in Cloud, Fog, and Mobile Edge Networks

Tudesco,

Deshpande,

Laghari

et al. 2024

Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection

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A conceptual framework to evaluate human-robot collaboration

Cited by 147 publications

References 65 publications

The ANEMONE: Theoretical Foundations for UX Evaluation of Action and Intention Recognition in Human-Robot Interaction

The ANEMONE: Theoretical Foundations for UX Evaluation of Action and Intention Recognition in Human-Robot Interaction

Success factors for introducing industrial human-robot interaction in practice: an empirically driven framework

Utilization of Deep Learning Models for Safe Human‐Friendly Computing in Cloud, Fog, and Mobile Edge Networks

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