UINav: A maker of UI automation agents

An automation system that can execute natural language instructions by driving the user interface (UI) of an application can benefit users, especially when situationally or permanently impaired. Traditional automation systems (manual scripting, programming by demonstration tools, etc.) do not produce generalizable models that can tolerate changes in the UI or task workflow. Machine-learned automation agents generalize better, but either work only in simple, hand-crafted applications or rely on large pre-trained models, which may be too computationally expensive to run on mobile devices. In this paper, we propose \emph{UINav}, a demonstration-based agent maker system. UINav agents are lightweight enough to run on mobile devices, yet they achieve high success rates with a modest number of task demonstrations. To minimize the number of task demonstrations, UINav includes a referee model that allows users to receive immediate feedback on tasks where the agent is failing to best guide efforts to collect additional demonstrations. Further, UINav adopts macro actions to reduce an agent's state space, and augments human demonstrations to increase the diversity of training data. Our evaluation demonstrates that with an average of 10 demonstrations per task UINav can achieve an accuracy of 70\% or higher, and that with enough demonstrations it can achieve near-perfect success rates on 40+ different tasks.