Summary of Androidlab: Training and Systematic Benchmarking Of Android Autonomous Agents, by Yifan Xu et al.
AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents
by Yifan Xu, Xiao Liu, Xueqiao Sun, Siyi Cheng, Hao Yu, Hanyu Lai, Shudan Zhang, Dan Zhang, Jie Tang, Yuxiao Dong
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed AndroidLab framework aims to systematically train and evaluate open-source and closed-source Android agents. The framework includes an operation environment with different modalities, action space, and a reproducible benchmark that supports both large language models (LLMs) and multimodal models (LMMs) in the same action space. The benchmark comprises predefined Android virtual devices and 138 tasks across nine apps built on these devices. By utilizing this framework, the researchers develop an Android Instruction dataset and train six open-source LLMs and LMMs, significantly improving their average success rates for both LLMs (from 4.59% to 21.50%) and LMMs (from 1.93% to 13.28%). AndroidLab is an open-sourced and publicly available framework at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new framework called AndroidLab that helps train and test Android agents, like robots or virtual assistants. This framework includes different environments and tasks for the agents to learn from. The researchers used this framework to improve the performance of six language models by training them on a specific task. They saw big improvements in how well the models did their jobs. |
