Service robots for personal use in the home and the workplace require end-user development solutions for swiftly scripting robot tasks as the need arises. Many existing solutions preserve ease, efficiency, and convenience through simple programming interfaces or by restricting task complexity. Others facilitate meticulous task design but often do so at the expense of simplicity and efficiency. There is a need for robot programming solutions that reconcile the complexity of robotics with the on-the-fly goals of end-user development. In response to this need, we present a novel, multimodal, and on-the-fly development system, Tabula. Inspired by a formative design study with a prototype, Tabula leverages a combination of spoken language for specifying the core of a robot task and sketching for contextualizing the core. The result is that developers can script partial, sloppy versions of robot programs to be completed and refined by a program synthesizer. Lastly, we demonstrate our anticipated use cases of Tabula via a set of application scenarios.

Human-robot interaction designers and developers navigate a complex design space, which creates a need for tools that support intuitive design processes and harness the programming capacity of state-of-the-art authoring environments. We introduce Figaro, an expressive tabletop authoring environment for mobile robots, inspired by shadow puppetry, that provides designers with a natural, situated representation of human-robot interactions while exploiting the intuitiveness of tabletop and tangible programming interfaces. On the tabletop, Figaro projects a representation of an environment. Users demonstrate sequences of behaviors, or scenes, of an interaction by manipulating instrumented figurines that represent the robot and the human. During a scene, Figaro records the movement of figurines on the tabletop and narrations uttered by users. Subsequently, Figaro employs real-time program synthesis to assemble a complete robot program from all scenes provided. Through a user study, we demonstrate the ability of Figaro to support design exploration and development for human-robot interaction.

Social robots have varied effectiveness when interacting with humans in different interaction contexts. A robot programmed to escort individuals to a different location, for instance, may behave more appropriately in a crowded airport than a quiet library, or vice versa. To address these issues, we exploit ideas from program synthesis and propose an approach to transforming the structure of hand-crafted interaction programs that uses user-scored execution traces as input, in which end users score their paths through the interaction based on their experience. Additionally, our approach guarantees that transformations to a program will not violate task and social expectations that must be maintained across contexts. We evaluated our approach by adapting a robot program to both real-world and simulated contexts and found evidence that making informed edits to the robot's program improves user experience.

Robots must exercise socially appropriate behavior when interacting with humans. How can we assist interaction designers to embed socially appropriate and avoid socially inappropriate behavior within human-robot interactions? We propose a multi-faceted interaction-design approach that intersects human-robot interaction and formal methods to help us achieve this goal. At the lowest level, designers create interactions from scratch and receive feedback from formal verification, while higher levels involve automated synthesis and repair of designs. In this extended abstract, we discuss past, present, and future work within each level of our design approach.

Computer science is in increasing demand at the K-12 level. However, due to a lack of qualified educators, numerous programs (e.g., Google's CS4HS, NSF's CSForAll) have been created to train teachers and increase student access to computer science. Although these programs have seen some success, schools in areas with lower population density continue to struggle to offer computer science opportunities. These schools face unique challenges due to their low population density, including increasing teaching demands in core subjects, smaller teaching staff, and a smaller pipeline of students, making it difficult to justify offering a course in computer science. This poster draws on our experience of collaborating with K-12 teachers in rural schools in western Wisconsin to help mitigate these unique challenges. We have obtained two Google CS4HS grants to hold educational computer science workshops with teachers from across the region. These workshops allowed us to work closely with teachers and help them become more comfortable with incorporating computer science into their classrooms, while also helping us to form relationships with both teachers and administrators in a number of districts. Building on these relationships, we are taking the next step of working more closely with individual school districts to form professional learning communities. Our goal in this work is to connect K-12 teachers within each district and help facilitate a robust pipeline of students interested in computer science and justify offering AP CS courses at the high school level.

Designing and implementing human-robot interactions requires numerous skills, from having a rich understanding of social interactions and the capacity to articulate their subtle requirements, to the ability to then program a social robot with the many facets of such a complex interaction. Although designers are best suited to develop and implement these interactions due to their inherent understanding of the context and its requirements, these skills are a barrier to enabling designers to rapidly explore and prototype ideas: it is impractical for designers to also be experts on social interaction behaviors, and the technical challenges associated with programming a social robot are prohibitive. In this work, we introduce Synthé, which allows designers to act out, or bodystorm, multiple demonstrations of an interaction. These demonstrations are automatically captured and translated into prototypes for the design team using program synthesis. We evaluate Synthé in multiple design sessions involving pairs of designers bodystorming interactions and observing the resulting models on a robot. We build on the findings from these sessions to improve the capabilities of Synthé and demonstrate the use of these capabilities in a second design session.

As social agents, robots designed for human interaction must adhere to human social norms. How can we enable designers, engineers, and roboticists to design robot behaviors that adhere to human social norms and do not result in interaction breakdowns? In this paper, we use automated formal-verification methods to facilitate the encoding of appropriate social norms into the interaction design of social robots and the detection of breakdowns and norm violations in order to prevent them. We have developed an authoring environment that utilizes these methods to provide developers of social-robot applications with feedback at design time and evaluated the benefits of their use in reducing such breakdowns and violations in human-robot interactions. Our evaluation with application developers (N=9) shows that the use of formal-verification methods increases designers' ability to identify and contextualize social-norm violations. We discuss the implications of our approach for the future development of tools for effective design of social-robot applications.

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.

This paper details the creation of a two-day computer science and robotics outreach course aimed at simultaneously engaging youth (children, ages 9-14) and senior (their grandparents, ages 55+) students. Our goal is to encourage enthusiasm for science and technology in students of all ages as well as provide practical instruction regarding common computer science concepts, including variables, loops, and boolean logic. To this end, we ground our course in the emerging field of social robotics, which enables the design of several multidisciplinary hands-on activities for students. We report on a four-year experience in the development of our course, which has been offered twelve times and involved over 210 youth and senior students. Our work presents a discussion regarding the challenges in designing a course for students from diverse ages, guidelines for creating similar courses, and a reflection on how we might improve our own class. The activities and project code developed for our course are available online as open-source resources.

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.

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.

As robots collaborate with humans in increasingly diverse environments, they will need to effectively refer to objects of joint interest and adapt their references to various physical, environmental, and task conditions. Humans use a broad range of deictic gestures-gestures that direct attention to collocated objects, persons, or spaces-that include pointing, touching, and exhibiting to help their listeners understand their references. These gestures offer varying levels of support under different conditions, making some gestures more or less suitable for different settings. While these gestures offer a rich space for designing communicative behaviors for robots, a better understanding of how different deictic gestures affect communication under different conditions is critical for achieving effective human-robot interaction. In this paper, we seek to build such an understanding by implementing six deictic gestures on a humanlike robot and evaluating their communicative effectiveness in six diverse settings that represent physical, environmental, and task conditions under which robots are expected to employ deictic communication. Our results show that gestures which come into physical contact with the object offer the highest overall communicative accuracy and that specific settings benefit from the use of particular types of gestures. Our results highlight the rich design space for deictic gestures and inform how robots might adapt their gestures to the specific physical, environmental, and task conditions.

Over the last decade, the cost of producing genomic sequences has dropped dramatically due to the current so-called next-generation sequencing methods. However, these next-generation sequencing methods are critically dependent on fast and sophisticated data processing methods for aligning a set of query sequences to a reference genome using rich string matching models. The focus of this work is on the design, development and evaluation of a data processing system for this crucial “short read alignment” problem. Our system, called WHAM, employs hash-based indexing methods and bitwise operations for sequence alignments. It allows rich match models and it is significantly faster than the existing state-of-the-art methods. In addition, its relative speedup over the existing method is poised to increase in the future in which read sequence lengths will increase.

Over the last decade the cost of producing genomic sequences has dropped dramatically due to the current so called "next-gen" sequencing methods. However, these next-gen sequencing methods are critically dependent on fast and sophisticated data processing methods for aligning a set of query sequences to a reference genome using rich string matching models. The focus of this work is on the design, development and evaluation of a data processing system for this crucial "short read alignment" problem. Our system, called WHAM, employs novel hash-based indexing methods and bitwise operations for sequence alignments. It allows richer match models than existing methods and it is significantly faster than the existing state-of-the-art method. In addition, its relative speedup over the existing method is poised to increase in the future in which read sequence lengths will increase. The WHAM code is available at http://www.cs.wisc.edu/wham/.