Computational Workflows for Designing Input Devices

TL;DR

This paper introduces a holistic computational workflow for designing input devices that automates and optimizes the process, reducing human bias and increasing efficiency through multi-objective optimization and user modeling.

Contribution

It presents a novel computational input design framework combining optimization and reinforcement learning to automate and improve input device design.

Findings

Designed a push-button that outperforms baselines

Workflow generalizes to various input devices

Achieves high efficiency and automation in design process

Abstract

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.