Projekter pr. år
Abstract
Trust in automation is usually assessed with post-interaction questionnaires. For human robot collaboration it would be beneficial to assess the trust level during the interaction to adjust the robot's collaboration behavior to the user expectations. In this paper we investigate if trust can be estimated from observable behavior like movements during the interaction with a large industrial manipulator. To this end, we report on a data collection for two tasks during collaborative draping, the transport of large cut pieces and the actual draping process in close proximity to the robot. The data is used to train and compare different deep learning models. Results show that automatic trust estimation is feasible, which opens up to using trust as a parameter for informing the interaction with robots.
Originalsprog | Engelsk |
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Titel | Proceedings of ICRA 2024 |
Antal sider | 6 |
Forlag | IEEE |
Status | Accepteret/In press - 2024 |
Begivenhed | 2024 IEEE International Conference on Robotics and Automation - Pacifico, Yokohama, Japan Varighed: 13 maj 2024 → 17 maj 2024 https://2024.ieee-icra.org/ |
Konference
Konference | 2024 IEEE International Conference on Robotics and Automation |
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Lokation | Pacifico |
Land/Område | Japan |
By | Yokohama |
Periode | 13/05/2024 → 17/05/2024 |
Internetadresse |
Fingeraftryk
Dyk ned i forskningsemnerne om 'Automatic Trust Estimation From Movement Data in Industrial Human-Robot Collaboration Based on Deep Learning'. Sammen danner de et unikt fingeraftryk.Projekter
- 1 Igangværende
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Drapebot: Collaborative Draping of Carbon Fiber Parts
01/01/2021 → 31/12/2024
Projekter: Projekt › Forskning
Forskningsdatasæt
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Benchmark movement data set for trust assessment in human robot collaboration
Rehm, M. (Ophavsperson), Hald, K. (Ophavsperson) & Pontikis, I. (Ophavsperson), Zenodo, 2 okt. 2023
DOI: 10.5281/zenodo.8224067, https://doi.org/10.5281/zenodo.8224067
Datasæt