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Cited by 10Teleoperation and Haptic SystemsRobot Manipulation and LearningHand Gesture Recognition Systems

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Human-Machine Interface for Remote Training of Robot Tasks

Open access
Spranger, Jordi, Buzatoiu, Roxana, Polydoros, Athanasios, Nalpantidis, Lazaros, Boukas, Evangelos·2018·2018 IEEE International Conference on Imaging Systems and Techniques (IST)
DOI 10.1109/ist.2018.8577081
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Abstract

Regardless of their industrial or research application, the streamlining of robot operations is limited by the proximity of experienced users to the actual hardware. Be it massive open online robotics courses, crowd-sourcing of robot task training, or remote research on massive robot farms for machine learning, the need to create an apt remote Human-Machine Interface is quite prevalent. The paper at hand proposes a novel solution to the programming/training of remote robots employing an intuitive and accurate user-interface which offers all the benefits of working with real robots without imposing delays and inefficiency. The system includes: a vision-based 3D hand detection and gesture recognition subsystem, a simulated digital twin of a robot as visual feedback, and the “remote” robot learning/executing trajectories using dynamic motion primitives. Our results indicate that the system is a promising solution to the problem of remote training of robot tasks.

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Crossref · 10.1109/ist.2018.8577081

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  • In the reviewed articles, DT was considered helpful for safety reasons in human-robot interaction, monitoring of smart wearable equipment, manufacturing of a produced part, VR visualization platform creation, and HMI visualization environments.

    “- 6) DT was also considered helpful for 'Safety reasons' in human-robot interaction [61,63,64], in monitoring of a smart wearable equipment [67], or in the manufacturing process of a produced part [66], within a virtual reality (VR) visualization platform creation [65] or with a HMI visualization environment [60].”

    From Review of digital twin applications in manufacturing (2019) · cites this paper as [66]

  • Five reviewed articles use a 3D dynamical Finite Element Method (FEM)-based model.

    “Five articles use a 3D dynamical Finite Element Method (FEM)-based model [32,34,38,55,66]”

    From Review of digital twin applications in manufacturing (2019) · cites this paper as [66]

Citations

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Research works printed but not linked: 31

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Metadata

Authors
Spranger, J., Buzatoiu, R., Polydoros, A., Nalpantidis, L., & Boukas, E.
Affiliations
  • Aalborg University (DK)
  • Universität Innsbruck (AT)
ORCID
Venue
2018 IEEE International Conference on Imaging Systems and Techniques (IST)
Publisher
IEEE
Published
2018-10
Pages
1-5
DOI
10.1109/ist.2018.8577081
OpenAlex
W2893282623
Cited by
10
References
0
Citations by year
2026: 1 · 2024: 2 · 2023: 3 · 2020: 2 · 2019: 2
FWCI
2.70
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE International Conference on Imaging Systems and Techniques (IST) (publishedVersion)
  • VBN Forskningsportal (Aalborg Universitet) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Engineering › Mechanical Engineering › Teleoperation and Haptic Systems
  • Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
  • Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
Keywords
human-machine interaction, gesture recognition, digital twin, dynamic motion primitives, vision-based interaction

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