A system for human-in-the-loop simulation of industrial collaborative robot applications

Metzner, Maximilian; Utsch, Daniel; Walter, Matthias; Hofstetter, Christian; Ramer, Christina; Blank, Andreas; Franke, Jörg

doi:10.1109/case48305.2020.9216766

Cited by 16 publications

(12 citation statements)

References 10 publications

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“…The research on human-in-the-loop was proposed for two purposes, utilizing fewer low-level manual operations and utilizing more higher-level human intelligence. Unlike the studies on dataset construction and methodologies, the [152] 2001 Security system for traditional tasks Cranor [153] 2008 Security system for traditional tasks MacHiry et al [154] 2013 Software testing Kovashka et al [155] 2015 Image-based searching engine Louis Rosenberg [156] 2016 Crowdsourcing Yan et al [157] 2017 Software testing Wogalter [158] 2018 Security system for traditional tasks MA [159] 2018 AI model optimizing in training Salam et al [160] 2019 AI model optimizing in testing Plummer et al [161] 2019 Image-based searching engine Fredrik Wrede & Andreas Hellander [162] 2019 Stochastic gene regulatory Singh and Mahmoud [163] 2020 Security system for traditional tasks Demartiniet al [164] 2020 Security system for modern tasks ODEKERKEN & BEX [165] 2020 Security system for modern tasks Bohme et al [166] 2020 Program repairing Renner [167] 2020 AI model optimizing in the whole steps Demirel [168] 2020 Digital human modeling Metzner et al [169] 2020 Simulated human-robot collaboration system Zhou et al [170] 2020 Melody composition interactive system Polisetty & Avinesh [171] 2020 Searching engine Zhu et al [172] 2020 Renal Pathology Li et al [173] 2020 Model checking usage of human-in-the-loop on system construction is comprehensive, and the works are different among the application scenarios. This section will review the human-in-the-loop on system construction divided by system components and applications in this section, following the baseline of engineering requirements.…”

Section: System Construction and Applicationmentioning

confidence: 99%

A Survey of Human-in-the-loop for Machine Learning

Wu,

Xiao,

Sun

et al. 2021

Preprint

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Human-in-the-loop aims to train an accurate prediction model with minimum cost by integrating human knowledge and experience. Humans can provide training data for machine learning applications and directly accomplish some tasks that are hard for computers in the pipeline with the help of machine-based approaches. In this paper, we survey existing works on human-in-the-loop from a data perspective and classify them into three categories with a progressive relationship: (1) the work of improving model performance from data processing, (2) the work of improving model performance through interventional model training, and (3) the design of the system independent human-in-the-loop. Using the above categorization, we summarize major approaches in the field, along with their technical strengths/ weaknesses, we have simple classification and discussion in natural language processing, computer vision, and others. Besides, we provide some open challenges and opportunities. This survey intends to provide a high-level summarization for human-in-the-loop and motivates interested readers to consider approaches for designing effective human-in-the-loop solutions.

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Section: System Construction and Applicationmentioning

confidence: 99%

A Survey of Human-in-the-loop for Machine Learning

Wu,

Xiao,

Sun

et al. 2021

Preprint

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“…Five citations are from 2018 (Janis and Alias 2018;Marszalek and Volz 2018;Mizuchi and Inamura 2018;Ottogalli et al 2018;Shu et al 2018), eight citations are from 2019 (Chang et al 2019;Gao et al 2019bGao et al , 2019aMoon et al 2019;Ojstersek et al 2019;Randon and Cecconello 2019;Shu and Sziebig 2019;Yan et al 2019) and five citations are from 2020 (Anik et al 2020;Malik et al 2020;Metzner, Utsch, et al 2020;Peruzzini et al 2020).…”

Section: Researchgatementioning

confidence: 99%

“…Among those, one is the journal article "Strategic systematic for software development in industry 4.0" (Oliveira Valério et al 2020). Four are conference papers (Arntz and Eimler 2020; H. P. Etzi et al 2019;Metzner, Utsch, et al 2020), specifically "Experiencing AI in VR: A Qualitative Study on Designing a Human-Machine Collaboration Scenario" (Arntz and Eimler 2020), "Use of Virtual Reality in Product Development by Distributed Teams" (H. P. Balzerkiewitz and Stechert 2020), "A system for human-in-the-loop simulation of industrial collaborative robot applications" (Metzner, Utsch, et al 2020) and "Using virtual reality to test human-robot interaction during a collaborative task" (Etzi et al 2019). Four are theses (Gong 2019;Gustavsson 2020; Lloyd Reyson Robles 2020; Tolman 2018).…”

Section: Researchgatementioning

confidence: 99%

“…Semantic Scholar lists 19 citations for the paper. Four are journal articles (Anik et al 2020;Malik et al 2020;Oliveira Valério et al 2020;Peruzzini et al 2020), thirteen are conference papers (H.-P. Chang et al 2019;Gao et al 2019b;Janis and Alias 2018;Metzner, Krieg, et al 2020;Metzner, Utsch, et al 2020;Mizuchi and Inamura 2018;Moon et al 2019;Ojstersek et al 2019;Ottogalli et al 2018;Shu et al 2018;Shu and Sziebig 2019;Yan et al 2019) and one is a preprint (Gao et al 2019a). One citation is wrongly directed to the whole conference proceedings containing the conference paper "Intuitive, VR-and Gesturebased Physical Interaction with Virtual Commissioning Simulation Models" (Metzner, Krieg, et al 2020).…”

Section: Semantic Scholarmentioning

confidence: 99%

“…Maximilian Metzner and colleagues in 2020 publish two conference papers, namely "A system for humanin-the-loop simulation of industrial collaborative robot applications" (Metzner, Utsch, et al 2020) and "Intuitive, VR-and Gesture-based Physical Interaction with Virtual Commissioning Simulation Models" (Metzner, Krieg, et al 2020).…”

Section: Citation Clustersmentioning

confidence: 99%

See 2 more Smart Citations

Citation review of Human-Machine Collaboration in Virtual Reality for Adaptive Production Engineering by de Giorgio et al.

Giorgio¹,

Onori²

2020

Preprint

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This article argues that despite a citation review is a rarely used research tool, this can be very useful to assess the impact of new research topics, both from the future research direction and the bibliometric perspectives. An explorative study is presented around the research area marked as Industry 4.0 with the conference paper mentioned in the title of this citation review. Even though the given reference paper is relatively recent, there are already twenty-seven citations listed among three different scholar databases. These are Google Scholar, ResearchGate and Semantic Scholar. In light of this, the article provides a bibliometric confirmation and analysis for the progression of the line of research adopted by de Giorgio et al. in the exploration of non-traditional methods using virtual reality technology and human-robot collaboration for adaptive applications in Industry 4.0. Furthermore, it represents a model for the authors’ self-development and an example of an unconventional approach to scientific work that may help improve related bibliometric research and scholar database strategies to index new articles and topics in the future.

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