Input Matters: How Telepresence Control Devices Affect Performance, Sense of Control, and User Experience

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Authors: Pratyusha Ghosh, Sachiko Matsumoto, Vivek Gupte, Robert Bloom, Alex Chow, Nandini Desai, Donovan Le, Tania K. Morimoto, Laurel D. Riek
Cited by: 2
References: 32

Abstract

Control devices are essential in shaping the user experience of telepresence robot operators. This is especially true for mobile telemanipulator robots (MTRs), which offer greater opportunities for social interaction compared to mobile or tabletop telepresence robots, but are more difficult to control. This increased difficulty can diminish key aspects of user experience, such as sense of control (SoC), which many users value over performance. However, the usability of telepresence control devices has been critically overlooked in HRI research. In a between-subjects study (n = 63), we investigate how three widely used control devices (mouse, gamepad, haptic controller) affect critical aspects of teleoperation: perceived SoC, safety, usability, and cognitive load; and task performance. Participants remotely controlled an MTR (Stretch) to wait on a customer in a social telepresence setting (cafe). We found that the type of control device significantly impacts both task performance and SoC in direct teleoperation, with the mouse having the best performance/SoC. There were notable tradeoffs in speeds, errors, and task completion times, and participants with higher SoC performed significantly better than those with lower SoC. We provide participant-informed suggestions for future control device design, such as allowing users to reconfigure its physical form, input sensitivity, and controls to align with video game conventions. By foregrounding the role of control devices, our work contributes to ongoing conversations in HRI around the tradeoffs between autonomy, usability, and user agency in social telepresence.

Fields of Study & Subjects

Virtual Reality Applications and Impacts Human-Automation Interaction and Safety Personal Information Management and User Behavior

Publication details

Journal / source
Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction
Publisher
ACM
Pages
1160–1170
Publication date
10 March 2026