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May 23, 2024
Autonomous agents that execute human tasks by controlling computers can enhance human productivity and application accessibility. Yet, progress in this field will be driven by realistic and reproducible benchmarks. We present AndroidWorld, a fully functioning Android environment that provides reward signals for 116 programmatic task workflows across 20 real world Android applications. Unlike existing interactive environments, which provide a static test set, AndroidWorld dynamically constructs tasks that are parameterized and expressed in natural language in unlimited ways, thus enabling testing on a much larger and realistic suite of tasks. Reward signals are derived from the computer's system state, making them durable across task variations and extensible across different apps. To demonstrate AndroidWorld's benefits and mode of operation, we introduce a new computer control agent, M3A. M3A can complete 30.6% of the AndroidWorld's tasks, leaving ample room for future work. Furthermore, we adapt a popular desktop web agent to work on Android, which we find to be less effective on mobile, suggesting future research is needed to achieve universal, cross-domain agents. Finally, we conduct a robustness analysis by testing M3A against a range of task variations on a representative subset of tasks, demonstrating that variations in task parameters can significantly alter the complexity of a task and therefore an agent's performance, highlighting the importance of testing agents under diverse conditions. AndroidWorld and the experiments in this paper are available at https://github.com/google-research/android_world.
UINav: A maker of UI automation agents
Dec 15, 2023An 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.