{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/androidlab-training-and-systematic","title":"AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents","arxiv_id":"2410.24024","date":"2024-10-31","proceeding":null,"authors":["Yifan Xu","Xiao Liu","Xueqiao Sun","Siyi Cheng","Hao Yu","Hanyu Lai","Shudan Zhang","Dan Zhang","Jie Tang","Yuxiao Dong"],"abstract":"Autonomous agents have become increasingly important for interacting with the real world. Android agents, in particular, have been recently a frequently-mentioned interaction method. However, existing studies for training and evaluating Android agents lack systematic research on both open-source and closed-source models. In this work, we propose AndroidLab as a systematic Android agent framework. It includes an operation environment with different modalities, action space, and a reproducible benchmark. It supports both large language models (LLMs) and multimodal models (LMMs) in the same action space. AndroidLab benchmark includes predefined Android virtual devices and 138 tasks across nine apps built on these devices. 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