Across history and cultures, robots have been envisioned as assistants working alongside people. Following this vision, an emerging family of products-collaborative manufacturing robots-is enabling human and robot workers to work side by side as collaborators in manufacturing tasks. Their introduction presents an opportunity to better understand people's interactions with and perceptions of a robot "co-worker" in a real-world setting to guide the design of these products. In this paper, we present findings from an ethnographic field study at three manufacturing sites and a Grounded Theory analysis of observations and interviews. Our results show that, even in this safety-critical manufacturing setting, workers relate to the robot as a social entity and rely on cues to understand the robot's actions, which we observed to be critical for workers to feel safe when near the robot. These findings contribute to our understanding of interactions with robotic products in real-world settings and offer important design implications.

In everyday interactions, humans naturally exhibit behavioral cues, such as gaze and head movements, that signal their intentions while interpreting the behavioral cues of others to predict their intentions. Such intention prediction enables each partner to adapt their behaviors to the intent of others, serving a critical role in joint action where parties work together to achieve a common goal. Among behavioral cues, eye gaze is particularly important in understanding a person's attention and intention. In this work, we seek to quantify how gaze patterns may indicate a person's intention. Our investigation was contextualized in a dyadic sandwich-making scenario in which a “worker” prepared a sandwich by adding ingredients requested by a “customer.” In this context, we investigated the extent to which the customers' gaze cues serve as predictors of which ingredients they intend to request. Predictive features were derived to represent characteristics of the customers' gaze patterns. We developed a support vector machine-based (SVM-based) model that achieved 76% accuracy in predicting the customers' intended requests based solely on gaze features. Moreover, the predictor made correct predictions approximately 1.8 s before the spoken request from the customer. We further analyzed several episodes of interactions from our data to develop a deeper understanding of the scenarios where our predictor succeeded and failed in making correct predictions. These analyses revealed additional gaze patterns that may be leveraged to improve intention prediction. This work highlights gaze cues as a significant resource for understanding human intentions and informs the design of real-time recognizers of user intention for intelligent systems, such as assistive robots and ubiquitous devices, that may enable more complex capabilities and improved user experience.

Robotic products are envisioned to offer rich interactions in a range of environments. While their specific roles will vary across applications, these products will draw on fundamental building blocks of interaction, such as greeting people, narrating information, providing instructions, and asking and answering questions. In this paper, we explore how such building blocks might serve as interaction design patterns that enable design exploration and prototyping for human-robot interaction. To construct a pattern library, we observed human interactions across different scenarios and identified seven patterns, such as question-answer pairs. We then designed and implemented Interaction Blocks, a visual authoring environment that enabled prototyping of robot interactions using these patterns. Design sessions with designers and developers demonstrated the promise of using a pattern language for designing robot interactions, confirmed the usability of our authoring environment, and provided insights into future research on tools for human-robot interaction design.

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To design computer-supported collaborative work (CSCW) systems that effectively support remote collaboration, designers need a better understanding of how people collaborate face-to-face and the mechanisms that they use to coordinate their actions. While research in CSCW has studied how specific social cues might facilitate collaboration in specific tasks, such as the role of gestures in video instruction, less is known about how a range of communicative cues might facilitate activities across many collaborative settings. In this paper, we model the predictive relationships between facial, gestural, and vocal cues and collaborative outcomes in three different tasks, drawing conclusions on how each cue might contribute to these outcomes in a given task and how such relationships generalize across tasks. The resulting models provide a quantitative understanding of the relative importance of each type of social cue in predicting collaborative outcomes, as well as a more thorough understanding of how the role of each social cue changes across tasks. Additionally, our results provide confirmation and illumination of prior findings in face-to-face and computer-mediated communication research.