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

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