人-机器人共享性：机器人群体辅助喂食的咬食时机预测
Human-Robot Commensality: Bite Timing Prediction for Robot-Assisted Feeding in Groups
摘要：
我们开发了数据驱动的模型来预测机器人在社交用餐场景中何时应该进食。对于行动不便的人来说，能够与朋友和家人一起独立吃饭被认为是最难忘和最重要的活动之一。虽然现有的机器人系统为行动不便的人提供食物，但专注于单独用餐，而共同用餐，即一起吃饭的行为，通常是选择的做法。与其他人共享膳食引入了机器人的社会适当的咬定时的问题，即机器人在不破坏共享膳食的社会动态的情况下进食的适当定时。我们的关键见解是，考虑到社交线索的微妙平衡的咬时机策略可以在社交用餐场景中的机器人辅助喂食过程中实现无缝互动。我们通过收集一个包含30组三人一起吃饭的人与人的共同性数据集（HHCD）来解决这个问题。我们使用这个数据集来分析人与人之间的相互依赖行为，并在社交用餐场景中开发咬时间预测模型。我们还将这些模型转移到人类-机器人的共同性场景中。我们的用户研究表明，当我们的算法使用用餐者之间的多模态社会信号线索来模拟咬时间时，预测得到了改善。HHCD数据集、用户研究视频和代码可在https://emprise.cs.cornell.edu/hrcom/上获得
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
