Human-Robot Commensality: Bite Timing Prediction for Robot-Assisted Feeding in Groups

Jan Ondras, Abrar Anwar, Tong Wu, Fanjun Bu, Malte Jung, Jorge Jose Ortiz, Tapomayukh Bhattacharjee
Proceedings of The 6th Conference on Robot Learning, PMLR 205:921-933, 2023.

Abstract

We develop data-driven models to predict when a robot should feed during social dining scenarios. Being able to eat independently with friends and family is considered one of the most memorable and important activities for people with mobility limitations. While existing robotic systems for feeding people with mobility limitations focus on solitary dining, commensality, the act of eating together, is often the practice of choice. Sharing meals with others introduces the problem of socially appropriate bite timing for a robot, i.e. the appropriate timing for the robot to feed without disrupting the social dynamics of a shared meal. Our key insight is that bite timing strategies that take into account the delicate balance of social cues can lead to seamless interactions during robot-assisted feeding in a social dining scenario. We approach this problem by collecting a Human-Human Commensality Dataset (HHCD) containing 30 groups of three people eating together. We use this dataset to analyze human-human commensality behaviors and develop bite timing prediction models in social dining scenarios. We also transfer these models to human-robot commensality scenarios. Our user studies show that prediction improves when our algorithm uses multimodal social signaling cues between diners to model bite timing. The HHCD dataset, videos of user studies, and code are available at https://emprise.cs.cornell.edu/hrcom/

Cite this Paper

BibTeX
@InProceedings{pmlr-v205-ondras23a, title = {Human-Robot Commensality: Bite Timing Prediction for Robot-Assisted Feeding in Groups}, author = {Ondras, Jan and Anwar, Abrar and Wu, Tong and Bu, Fanjun and Jung, Malte and Ortiz, Jorge Jose and Bhattacharjee, Tapomayukh}, booktitle = {Proceedings of The 6th Conference on Robot Learning}, pages = {921--933}, year = {2023}, editor = {Liu, Karen and Kulic, Dana and Ichnowski, Jeff}, volume = {205}, series = {Proceedings of Machine Learning Research}, month = {14--18 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v205/ondras23a/ondras23a.pdf}, url = {https://proceedings.mlr.press/v205/ondras23a.html}, abstract = {We develop data-driven models to predict when a robot should feed during social dining scenarios. Being able to eat independently with friends and family is considered one of the most memorable and important activities for people with mobility limitations. While existing robotic systems for feeding people with mobility limitations focus on solitary dining, commensality, the act of eating together, is often the practice of choice. Sharing meals with others introduces the problem of socially appropriate bite timing for a robot, i.e. the appropriate timing for the robot to feed without disrupting the social dynamics of a shared meal. Our key insight is that bite timing strategies that take into account the delicate balance of social cues can lead to seamless interactions during robot-assisted feeding in a social dining scenario. We approach this problem by collecting a Human-Human Commensality Dataset (HHCD) containing 30 groups of three people eating together. We use this dataset to analyze human-human commensality behaviors and develop bite timing prediction models in social dining scenarios. We also transfer these models to human-robot commensality scenarios. Our user studies show that prediction improves when our algorithm uses multimodal social signaling cues between diners to model bite timing. The HHCD dataset, videos of user studies, and code are available at https://emprise.cs.cornell.edu/hrcom/} }
Endnote
%0 Conference Paper %T Human-Robot Commensality: Bite Timing Prediction for Robot-Assisted Feeding in Groups %A Jan Ondras %A Abrar Anwar %A Tong Wu %A Fanjun Bu %A Malte Jung %A Jorge Jose Ortiz %A Tapomayukh Bhattacharjee %B Proceedings of The 6th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2023 %E Karen Liu %E Dana Kulic %E Jeff Ichnowski %F pmlr-v205-ondras23a %I PMLR %P 921--933 %U https://proceedings.mlr.press/v205/ondras23a.html %V 205 %X We develop data-driven models to predict when a robot should feed during social dining scenarios. Being able to eat independently with friends and family is considered one of the most memorable and important activities for people with mobility limitations. While existing robotic systems for feeding people with mobility limitations focus on solitary dining, commensality, the act of eating together, is often the practice of choice. Sharing meals with others introduces the problem of socially appropriate bite timing for a robot, i.e. the appropriate timing for the robot to feed without disrupting the social dynamics of a shared meal. Our key insight is that bite timing strategies that take into account the delicate balance of social cues can lead to seamless interactions during robot-assisted feeding in a social dining scenario. We approach this problem by collecting a Human-Human Commensality Dataset (HHCD) containing 30 groups of three people eating together. We use this dataset to analyze human-human commensality behaviors and develop bite timing prediction models in social dining scenarios. We also transfer these models to human-robot commensality scenarios. Our user studies show that prediction improves when our algorithm uses multimodal social signaling cues between diners to model bite timing. The HHCD dataset, videos of user studies, and code are available at https://emprise.cs.cornell.edu/hrcom/
APA
Ondras, J., Anwar, A., Wu, T., Bu, F., Jung, M., Ortiz, J.J. & Bhattacharjee, T.. (2023). Human-Robot Commensality: Bite Timing Prediction for Robot-Assisted Feeding in Groups. Proceedings of The 6th Conference on Robot Learning, in Proceedings of Machine Learning Research 205:921-933 Available from https://proceedings.mlr.press/v205/ondras23a.html.

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