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Augmenting the Human-Robot Communication Channel in Shared Task Environments

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Collaboration Technologies and Social Computing (CollabTech 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12324))

Abstract

Adaptive robots that collaborate with humans in shared task environments are expected to enhance production efficiency and flexibility in a near future. In this context, the question of acceptance of such a collaboration by human workers is essential for a successful implementation. Augmenting the robot-to-human communication channel with situation-specific and explanatory information might increase the workers’ willingness to collaborate with artificial counterparts, as a robot that provides guidance and explanation might be perceived as more cooperative in a social sense. However, the effects of using different augmentation strategies and parameters have not yet been sufficiently explored. This paper examines the usage of augmenting industrial robots involved in shared task environments by conducting an evaluation in a virtual reality (VR) setting. The results provide a first step towards an iterative design process aiming to facilitate and enhance the collaboration between human’s and robot’s in industrial contexts.

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Acknowledgments

Many thanks go to Dustin Keßler, Dr. Carolin Straßmann, Nele Borgert, Dr. Laura Hoffmann and Sarah Zielinski for their advice, comments to the manuscript and encouragement while conducting this study. Additional thanks go to Dr. Ioannis Iossifidis, Sebastian Doliwa, Mehdi Cherbib, Clarissa Arlinghaus, Stefan Sommer and to all participants contributing to the study as well as to the reviewers.

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Arntz, A., Eimler, S.C., Hoppe, H.U. (2020). Augmenting the Human-Robot Communication Channel in Shared Task Environments. In: Nolte, A., Alvarez, C., Hishiyama, R., Chounta, IA., Rodríguez-Triana, M., Inoue, T. (eds) Collaboration Technologies and Social Computing . CollabTech 2020. Lecture Notes in Computer Science(), vol 12324. Springer, Cham. https://doi.org/10.1007/978-3-030-58157-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-58157-2_2

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