论文标题
人类机器人的态度：咬合机器人辅助进食的咬合时间预测
Human-Robot Commensality: Bite Timing Prediction for Robot-Assisted Feeding in Groups
论文作者
论文摘要
我们开发了数据驱动的模型，以预测机器人在社交就餐场景中应何时喂食。能够与朋友和家人独立吃饭被认为是行动不便的人最令人难忘，最重要的活动之一。虽然现有的机器人系统用于喂养具有流动性限制的人的重点是孤独的餐饮，但共同性，共同饮食的行为通常是选择的做法。与他人共享餐点介绍了机器人对机器人的社交叮咬时机的问题，即机器人喂食的适当时机而不会破坏共享餐点的社会动态。我们的关键见解是，考虑到社交线索的微妙平衡的咬合时序策略可能会导致在社交餐饮场景中在机器人辅助喂养过程中进行无缝互动。我们通过收集一个人类人类的尊贵数据集（HHCD）来解决这个问题，其中包含30组三人共同饮食。我们使用此数据集分析人类人类的赋形行为，并在社交用餐场景中开发咬合的时序预测模型。我们还将这些模型转移到人类机器人的态度场景中。我们的用户研究表明，当我们的算法使用食客之间的多模式社交信号线索来建模时，预测会有所改善。 HHCD数据集，用户研究的视频和代码可在https://emprise.cs.cornell.edu/hrcom/上找到
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/
