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In this paper we explore what role humans might play in designing tools for reinforcement learning (RL) agents to interact with the world. Recent work has explored RL methods that optimize a robot’s morphology while learning to control it, effectively dividing an RL agent’s environment into the external world and the agent’s interface with the world. Taking a user-centered design (UCD) approach, we explore the potential of a human, instead of an algorithm, redesigning the agent’s tool. Using UCD to design for a machine learning agent brings up several research questions, including what it means to understand an RL agent’s experience, beliefs, tendencies, and goals. After discussing these questions, we then present a system we developed to study humans designing a 2D racecar for an RL autonomous driver. We conclude with findings and insights from exploratory pilots with twelve users using this system. 

We describe a physical interactive system for human-robot collaborative design (HRCD) consisting of a tangible user interface (TUI) and a robotic arm that simultaneously manipulates the TUI with the human designer. In an observational study of 12 participants exploring a complex design problem together with the robot, we find that human designers have to negotiate both the physical and the creative space with the machine. They also often ascribe social meaning to the robot's pragmatic behaviors. Based on these findings, we propose four considerations for future HRCD systems: managing the shared workspace, communicating preferences about design goals, respecting different design styles, and taking into account the social meaning of design acts. 

This article describes Daphne, a virtual assistant for designing Earth observation distributed spacecraft missions. It is, to the best of our knowledge, the first virtual assistant for such application. The article provides a thorough description of Daphne, including its question answering system and the main features we have implemented to help system engineers design distributed spacecraft missions. In addition, the article describes a study performed at NASA’s Jet Propulsion Laboratory (JPL) to assess the usefulness of Daphne in this use case. The study was conducted with N = 9 subjects from JPL, who were asked to work on a mission design task with two versions of Daphne, one that was fully featured implementing the cognitive assistance functions, and one that only had the features one would find in a traditional design space exploration tool. After the task, they filled out a standard user experience survey, completed a test to assess how much they learned about the task, and were asked a number of questions in a semi-structured exit interview. Results of the study suggest that Daphne can help improve performance during system design tasks compared to traditional tools, while keeping the system usable. However, the study also raises some concerns with respect to a potential reduction in human learning due to the use of the cognitive assistant. The article ends with a list of suggestions for future development of virtual assistants for space mission design.