Collaborative robots, or cobots, represent a breakthrough technology designed for high-level (e.g., collaborative) interactions between workers and robots with capabilities for flexible deployment in industries such as manufacturing. Understanding how workers and companies use and integrate cobots is important to inform the future design of cobot systems and educational technologies that facilitate effective worker-cobot interaction. Yet, little is known about typical training for collaboration and the application of cobots in manufacturing. To close this gap, we interviewed nine experts in manufacturing about their experience with cobots. Our thematic analysis revealed that, contrary to the envisioned use, experts described most cobot applications as only low-level (e.g., pressing start/stop buttons) interactions with little flexible deployment, and experts felt traditional robotics skills were needed for collaborative and flexible interaction with cobots. We conclude with design recommendations for improved future robots, including programming and interface designs, and educational technologies to support collaborative use.
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Safety first: Developing a model of expertise in collaborative robotics
Rapid advances in technology also come with increased training needs
for people who engineer and interact with these technologies. One such technology is collaborative robots, cobots, which are designed to be safer and easier
to use than their traditional robotic counterparts. However, there have been few
studies of how people use cobots and even fewer identifying what a user must
know to properly set up and effectively use cobots for their manufacturing processes. In this study, we interviewed nine experts in robots and automation in
manufacturing settings. We employ a quantitative ethnographic approach to gain
qualitative insights into the cultural practices of robotics experts and corroborate
these stories with quantitative warrants. Both quantitative and qualitative analyses revealed that experts put safety first when designing and monitoring cobot
applications. This study improves our understanding of expert problem-solving
in collaborative robotics, defines an expert model that can serve as a basis for the
development of an authentic learning technology, and illustrates a useful method
for modeling expertise in vocational settings.
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- Award ID(s):
- 1822872
- NSF-PAR ID:
- 10248767
- Editor(s):
- Ruis, Andrew R.; Lee, Seung B.
- Date Published:
- Journal Name:
- Advances in Quantitative Ethnography: Second International Conference, ICQE 2020, Malibu, CA, USA, February 1-3, 2021, Proceedings
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/https://doi.org/10.1007/978-3-030-67788-6_21
