The effects of role transitions and adaptation in human–cobot collaboration

Lorenzo Vianello (), Serena Ivaldi (), Alexis Aubry () and Luka Peternel ()
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Lorenzo Vianello: University of Lorraine, CNRS, Inria, LORIA
Serena Ivaldi: University of Lorraine, CNRS, Inria, LORIA
Alexis Aubry: University of Lorraine, CNRS, CRAN
Luka Peternel: Delft University of Technology

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 5, No 7, 2005-2019

Abstract: Abstract Collaborative robots (cobots) have the potential to augment the productivity and life quality of human operators in the context of Industry 4.0 by providing them with physical assistance. For this reason, it is necessary to define the relationship between humans and cobots and to study how the two agents adapt to each other. However, to the best of our knowledge, literature is still missing insight into how humans perceive and react to changes in the cobot behavior (e.g. changes in the learned trajectory and in the role the robot assumes). Specifically, a study of how humans adapt to changing roles and control strategies of collaborating robots is missing. To fill this gap, we propose a human study in which 16 participants executed a collaborative human–robot sawing task where the cobot altered between three different control strategies. We examined human adaptation when cobot suddenly changed the control strategy from one to another, resulting in six experimental conditions. The experiments were performed on a setup involving Kuka LBR iiwa robotic arm. The results suggest that transition influences movement performance in the early stages and at steady state, subjects prefer to abandon modes that require more effort and they adapt faster to energy-demanding modes. Finally, for the specific task we studied, subjects tend to prefer collaborative modes to ones in which the robot assumes a fixed role.

Keywords: Collaborative robots; Adaptation; Human–robot physical interaction (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02104-5

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