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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. 

Collaborative robots promise to transform work across many industries and promote “human-robot teaming” as a novel paradigm. However, realizing this promise requires the understanding of how existing tasks, developed for and performed by humans, can be effectively translated into tasks that robots can singularly or human-robot teams can collaboratively perform. In the interest of developing tools that facilitate this process we present Authr, an end-to-end task authoring environment that assists engineers at manufacturing facilities in translating existing manual tasks into plans applicable for human-robot teams and simulates these plans as they would be performed by the human and robot. We evaluated Authr with two user studies, which demonstrate the usability and effectiveness of Authr as an interface and the benefits of assistive task allocation methods for designing complex tasks for human-robot teams. We discuss the implications of these findings for the design of software tools for authoring human-robot collaborative plans. 

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. 

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. 

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.