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End-user development (EUD) represents a key step towards making robotics accessible for experts and non-experts alike. Within academia, researchers investigate novel ways that EUD tools can capture, represent, visualize, analyze, and test developer intent. At the same time, industry researchers increasingly build and ship programming tools that enable customers to interact with their robots. However, despite this growing interest, the role of EUD within HRI is not well defined. EUD struggles to situate itself within a growing array of alternative approaches to application development, such as robot learning and teleoperation. EUD further struggles due to the wide range of individuals who can be considered end users, such as independent third-party application developers, consumers, hobbyists, or even employees of the robot manufacturer. Key questions remain such as how EUD is justified over alternate approaches to application development, which contexts EUD is most suited for, who the target users of an EUD system are, and where interaction between a human and a robot takes place, amongst many other questions. We seek to address these challenges and questions by organizing the frst End-User Development for Human-Robot Interaction (EUD4HRI) workshop at the 2024 International Conference of Human-Robot Interaction. The workshop will bring together researchers with a wide range of expertise across academia and industry, spanning perspectives from multiple subfields of robotics, with the primary goal being a consensus of perspectives about the role that EUD must play within human-robot interaction. 

As service robots become more capable of autonomous behaviors, it becomes increasingly important to consider how people will be able to communicate with a robot about what task it should perform and how to do the task. There has been a rise in attention to end-user development (EUD), where researchers create interfaces that enable non-roboticist end users to script tasks for autonomous robots to perform. Currently, state-of-the-art interfaces are largely constrained, often through simplified domains or restrictive end-user interaction. Motivated by our past qualitative design work exploring how to integrate a care robot in an assisted living community, we discuss challenges of EUD in this complex domain. One set of challenges stems from different user-facing representations, e.g., certain tasks may lend themselves better to a rule-based trigger-action representations, whereas other tasks may be easier to specify via a sequence of actions. The other stems from considering the needs of multiple stakeholders, e.g., caregivers and residents of the facility may all create tasks for the robot, but the robot may not be able to share information about all tasks with all residents due to privacy concerns. We present scenarios that illustrate these challenges and also discuss possible solutions. 

The study of goal-reasoning agents capable of integrated action and execution has received a great deal of attention in recent years. While practical implementations and theoretical insights of such agents have provided a wealth of flexible behavior in a variety of task environments, they tend to focus on complex environments that are far from classical planning assumptions. This paper formalizes classical planning problems where an agent can change its goal(s) during execution. We identify the minimal changes to classical planning and formalize a model that supports "classical goal reasoning." 

Human-centered environments provide affordances for and require the use of two-handed, or bimanual, manipulations. Robots designed to function in, and physically interact with, these environments have not been able to meet these requirements because standard bimanual control approaches have not accommodated the diverse, dynamic, and intricate coordinations between two arms to complete bimanual tasks. In this work, we enabled robots to more effectively perform bimanual tasks by introducing a bimanual shared-control method. The control method moves the robot’s arms to mimic the operator’s arm movements but provides on-the-fly assistance to help the user complete tasks more easily. Our method used a bimanual action vocabulary, constructed by analyzing how people perform two-hand manipulations, as the core abstraction level for reasoning about how to assist in bimanual shared autonomy. The method inferred which individual action from the bimanual action vocabulary was occurring using a sequence-to-sequence recurrent neural network architecture and turned on a corresponding assistance mode, signals introduced into the shared-control loop designed to make the performance of a particular bimanual action easier or more efficient. We demonstrate the effectiveness of our method through two user studies that show that novice users could control a robot to complete a range of complex manipulation tasks more successfully using our method compared to alternative approaches. We discuss the implications of our findings for real-world robot control scenarios.