Motivating Physical Activity via Competitive Human-Robot Interaction

Boling Yang, Golnaz Habibi, Patrick Lancaster, Byron Boots, Joshua Smith
Proceedings of the 5th Conference on Robot Learning, PMLR 164:839-849, 2022.

Abstract

This project aims to motivate research in competitive human-robot interaction by creating a robot competitor that can challenge human users in certain scenarios such as physical exercise and games. With this goal in mind, we introduce the Fencing Game, a human-robot competition used to evaluate both the capabilities of the robot competitor and user experience. We develop the robot competitor through iterative multi-agent reinforcement learning and show that it can perform well against human competitors. Our user study additionally found that our system was able to continuously create challenging and enjoyable interactions that significantly increased human subjects’ heart rates. The majority of human subjects considered the system to be entertaining and desirable for improving the quality of their exercise.

Cite this Paper

BibTeX
@InProceedings{pmlr-v164-yang22e, title = {Motivating Physical Activity via Competitive Human-Robot Interaction}, author = {Yang, Boling and Habibi, Golnaz and Lancaster, Patrick and Boots, Byron and Smith, Joshua}, booktitle = {Proceedings of the 5th Conference on Robot Learning}, pages = {839--849}, year = {2022}, editor = {Faust, Aleksandra and Hsu, David and Neumann, Gerhard}, volume = {164}, series = {Proceedings of Machine Learning Research}, month = {08--11 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v164/yang22e/yang22e.pdf}, url = {https://proceedings.mlr.press/v164/yang22e.html}, abstract = {This project aims to motivate research in competitive human-robot interaction by creating a robot competitor that can challenge human users in certain scenarios such as physical exercise and games. With this goal in mind, we introduce the Fencing Game, a human-robot competition used to evaluate both the capabilities of the robot competitor and user experience. We develop the robot competitor through iterative multi-agent reinforcement learning and show that it can perform well against human competitors. Our user study additionally found that our system was able to continuously create challenging and enjoyable interactions that significantly increased human subjects’ heart rates. The majority of human subjects considered the system to be entertaining and desirable for improving the quality of their exercise.} }
Endnote
%0 Conference Paper %T Motivating Physical Activity via Competitive Human-Robot Interaction %A Boling Yang %A Golnaz Habibi %A Patrick Lancaster %A Byron Boots %A Joshua Smith %B Proceedings of the 5th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2022 %E Aleksandra Faust %E David Hsu %E Gerhard Neumann %F pmlr-v164-yang22e %I PMLR %P 839--849 %U https://proceedings.mlr.press/v164/yang22e.html %V 164 %X This project aims to motivate research in competitive human-robot interaction by creating a robot competitor that can challenge human users in certain scenarios such as physical exercise and games. With this goal in mind, we introduce the Fencing Game, a human-robot competition used to evaluate both the capabilities of the robot competitor and user experience. We develop the robot competitor through iterative multi-agent reinforcement learning and show that it can perform well against human competitors. Our user study additionally found that our system was able to continuously create challenging and enjoyable interactions that significantly increased human subjects’ heart rates. The majority of human subjects considered the system to be entertaining and desirable for improving the quality of their exercise.
APA
Yang, B., Habibi, G., Lancaster, P., Boots, B. & Smith, J.. (2022). Motivating Physical Activity via Competitive Human-Robot Interaction. Proceedings of the 5th Conference on Robot Learning, in Proceedings of Machine Learning Research 164:839-849 Available from https://proceedings.mlr.press/v164/yang22e.html.

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