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Collaborative robots (cobots) are increasingly utilized within the manufacturing industry. However, despite the promise of collaboration and easier programming when compared to traditional industrial robots, cobots introduce new interaction paradigms that require more thought about the environment and distribution of work to fully realize their collaboration capabilities. Due to these additional requirements, cobots have been found to be underutilized for their collaboration capabilities in current manufacturing. Therefore, in order to make cobots more accessible and easy to use, new systems need to be developed that support users during interaction. In this research, we propose a set of tools that target the use of cobots for multiple groups of individuals that use them, in order to better support users and simplify cobot collaboration. 

Robots are ubiquitous in manufacturing settings from small-scale to large-scale. While collaborative robots (cobots) have signicant potential in these settings due to their exibility and ease of use, they can only reach their full potential when properly integrated. Specically, cobots need to be integrated in a manner that properly utilizes their strengths, improves the performance of the manufacturing process, and can be used in concert with human workers. Understanding how to properly integrate cobots into existing manufacturing workows requires careful consideration and the knowledge of roboticists, manufacturing engineers, and business administrators. In this work, we propose an approach to collaborating with manufacturers prior to the integration process that involves planning, analysis, development, and presentation of results. This approach ultimately allows manufacturers to make an informed choice about cobot integration within their facilities. We illustrate the application of this approach through a case study with a manufacturing collaborator and discuss insights learned throughout the process. 

Authored robotics applications have a diverse set of requirements for their authoring interfaces, being dependent on the underlying architecture of the program, the capability of the programmers and engineers using them, and the capabilities of the robot. Visual programming approaches have long been favored for both novice-level accessibility, and clear graphical representations, but current tools are limited in their customizability and ability to be integrated holistically into larger design interfaces. OpenVP attempts to address this by providing a highly congurable and customizable component library that can be integrated easily into other modern web-based applications. 

We argue for the use of Petri nets as a modeling language for the iterative development process of interactive robotic systems. Petri nets, particularly Timed Colored Petri nets (TCPNs), have the potential to unify various phases of the development process — design, specification, simulation, validation, implementation, and deployment. We additionally discuss future directions for creating a domain-specific variant of TCPNs tailored specifically for HRI systems development. 

We present a participatory design method to design human-robot interactions with older adults and its application through a case study of designing an assistive robot for a senior living facility. The method, called Situated Participatory Design (sPD), was designed considering the challenges of working with older adults and involves three phases that enable designing and testing use scenarios through realistic, iterative interactions with the robot. In design sessions with nine residents and three caregivers, we uncovered a number of insights about sPD that help us understand its benefits and limitations. For example, we observed how designs evolved through iterative interactions and how early exposure to the robot helped participants consider using the robot in their daily life. With sPD, we aim to help future researchers to increase and deepen the participation of older adults in designing assistive technologies. 

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. 

Demonstration is an effective end-user development paradigm for teaching robots how to perform new tasks. In this paper, we posit that demonstration is useful not only as a teaching tool, but also as a way to understand and assist end-user developers in thinking about a task at hand. As a first step toward gaining this understanding, we constructed a lightweight web interface to crowdsource step-by-step instructions of common household tasks, leveraging the imaginations and past experiences of potential end-user developers. As evidence of the utility of our interface, we deployed the interface on Amazon Mechanical Turk and collected 207 task traces that span 18 different task categories. We describe our vision for how these task traces can be operationalized as task models within end-user development tools and provide a roadmap for future work. 

Robots designed to interact with people in collaborative or social scenarios must move in ways that are consistent with the robot's task and communication goals. However, combining these goals in a naïve manner can result in mutually exclusive solutions, or infeasible or problematic states and actions. In this paper, we present Lively, a framework which supports configurable, real-time, task-based and communicative or socially-expressive motion for collaborative and social robotics across multiple levels of programmatic accessibility. Lively supports a wide range of control methods (i.e. position, orientation, and joint-space goals), and balances them with complex procedural behaviors for natural, lifelike motion that are effective in collaborative and social contexts. We discuss the design of three levels of programmatic accessibility of Lively, including a graphical user interface for visual design called LivelyStudio, the core library Lively for full access to its capabilities for developers, and an extensible architecture for greater customizability and capability. 

The introduction of collaborative robots (cobots) into the workplace has presented both opportunities and challenges for those seeking to utilize their functionality. Prior research has shown that despite the capabilities afforded by cobots, there is a disconnect between those capabilities and the applications that they currently are deployed in, partially due to a lack of effective cobot-focused instruction in the field. Experts who work successfully within this collaborative domain could offer insight into the considerations and process they use to more effectively capture this cobot capability. Using an analysis of expert insights in the collaborative interaction design space, we developed a set of Expert Frames based on these insights and integrated these Expert Frames into a new training and programming system that can be used to teach novice operators to think, program, and troubleshoot in ways that experts do. We present our system and case studies that demonstrate how Expert Frames provide novice users with the ability to analyze and learn from complex cobot application scenarios. 

Socially interactive robots present numerous unique programming challenges for interaction developers. While modern authoring tools succeed at making the authoring experience approachable and convenient for developers from a wide variety of backgrounds, they are less successful at targeting assistance to developers based on the specific task or interaction being authored. We propose interaction templates, a data-driven solution for (1) matching in-progress robot programs to candidate task or interaction models and then (2) providing assistance to developers by using the matched models to generate modifications to in-progress programs. In this paper, we present the various dimensions that define first how interaction templates might be used, then how interaction templates may be represented, and finally how they might be collected.