Computational Workflows for Designing Input Devices
This addresses the problem of slow, human-biased design workflows for developers and users of input devices, offering a novel automated approach.
The thesis tackled the inefficient and biased manual design of input devices by proposing Computational Input Design workflows, which use multi-objective optimization and meta-reinforcement learning to automate the process, resulting in Pareto-optimal designs that outperform known baselines.
Input devices, such as buttons and sliders, are the foundation of any interface. The typical user-centered design workflow requires the developers and users to go through many iterations of design, implementation, and analysis. The procedure is inefficient, and human decisions highly bias the results. While computational methods are used to assist various design tasks, there has not been any holistic approach to automate the design of input components. My thesis proposed a series of Computational Input Design workflows: I envision a sample-efficient multi-objective optimization algorithm that cleverly selects design instances, which are instantly deployed on physical simulators. A meta-reinforcement learning user model then simulates the user behaviors when using the design instance upon the simulators. The new workflows derive Pareto-optimal designs with high efficiency and automation. I demonstrate designing a push-button via the proposed methods. The resulting designs outperform the known baselines. The Computational Input Design process can be generalized to other devices, such as joystick, touchscreen, mouse, controller, etc.