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<!DOCTYPE html>
<html>
<head>
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<title>Empowering LLM to use smartphone</title>
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<section class="hero">
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<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">Empowering LLM to use Smartphone for Intelligent Task Automation</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">Hao Wen<sup>1</sup>,</span>
<span class="author-block">Yuanchun Li<sup>1,†</sup>,</span>
<span class="author-block">Guohong Liu<sup>1</sup>,</span>
<span class="author-block">Shanhui Zhao<sup>1,*</sup>,</span>
<span class="author-block">Tao Yu<sup>1,*</sup>,</span>
<span class="author-block">Toby Jia-Jun Li<sup>2</sup>,</span>
<span class="author-block">Shiqi Jiang<sup>3</sup>,</span>
<span class="author-block">Yunhao Liu<sup>1</sup>,</span>
<span class="author-block">Yaqin Zhang<sup>1</sup>,</span>
<span class="author-block">Yunxin Liu<sup>1</sup></span>
<!-- <span class="author-block">
<a href="FIRST AUTHOR PERSONAL LINK" target="_blank">First Author</a><sup>*</sup>,</span>
<span class="author-block">
<a href="SECOND AUTHOR PERSONAL LINK" target="_blank">Second Author</a><sup>*</sup>,</span>
<span class="author-block">
<a href="THIRD AUTHOR PERSONAL LINK" target="_blank">Third Author</a>
</span> -->
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup> Tsinghua University </span>
<!-- <span class="author-block"><sup>2</sup> Harbin Institute of Technology </span> -->
<span class="author-block"><sup>2</sup> University of Notre Dame </span>
<span class="author-block"><sup>3</sup> Microsoft Research Asia </span>
<span class="eql-cntrb"><small><br><sup>†</sup>Corresponding author: Yuanchun Li (liyuanchun@air.tsinghua.edu.cn).</small></span>
<span class="eql-cntrb"><small><br><sup>*</sup>Shanhui Zhao and Tao Yu were student interns at Tsinghua University.</small></span>
</div>
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<div class="publication-links">
<!-- Arxiv PDF link -->
<span class="link-block">
<a href="https://arxiv.org/pdf/2308.15272.pdf" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
<!-- Supplementary PDF link -->
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class="external-link button is-normal is-rounded is-dark">
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<span>Supplementary</span>
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<a href="https://arxiv.org/abs/2308.15272" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<!-- Github link -->
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<a href="https://github.com/MobileLLM/AutoDroid" target="_blank" class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Teaser video-->
<section class="hero teaser">
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<!-- </video> -->
<img src="static/images/overview.png" alt="MY ALT TEXT" />
<h2 class="subtitle has-text-centered">
The workflow of AutoDroid. We introduce AutoDroid, an LLM-powered end-to-end mobile task automation system to solve the aforementioned challenges.
In the offline stage, AutoDroid obtains app-specific knowledge by exploring UI relations and synthesizing simulated
tasks. In the online stage, AutoDroid continuously queries the memory-augmented LLMs to obtain guidance on the next
action. The task is completed by following the LLM-suggested actions. AutoDroid adopts several techniques to improve the
task completion rate and optimize the query cost.
</h2>
</div>
</div>
</section>
<!-- End teaser video -->
<!-- Paper abstract -->
<section class="section hero is-light">
<div class="container is-max-desktop">
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<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Mobile task automation is an attractive technique that aims to enable voice-based hands-free user interaction with
smartphones.
% Enabling natural language-based task automation on smartphones has been a dream of mobile system and application
developers for a long time.
However, existing approaches suffer from poor scalability due to the limited language understanding ability and the
non-trivial manual efforts required from developers or end-users.
The recent advance of large language models (LLMs) in language understanding and reasoning inspires us to rethink the
problem from a model-centric perspective, where task preparation, comprehension, and execution are handled by a unified
language model.
In this work, we introduce AutoDroid, a mobile task automation system that can handle arbitrary tasks on any Android
application without manual efforts.
The key insight is to combine the commonsense knowledge of LLMs and domain-specific knowledge of apps through automated
dynamic analysis.
The main components include a functionality-aware UI representation method that bridges the UI with the LLM,
exploration-based memory injection techniques that augment the app-specific domain knowledge of LLM, and a
multi-granularity query optimization module that reduces the cost of model inference.
We integrate AutoDroid with off-the-shelf LLMs including online GPT-4/GPT-3.5 and on-device Vicuna, and evaluate its
performance on a new benchmark for memory-augmented Android task automation with 158 common tasks. The results
demonstrated that AutoDroid is able to precisely generate actions with an accuracy of 90.9%, and complete tasks with a
success rate of 71.3%, outperforming the GPT-4-powered baselines by 36.4% and 39.7%. The demo, benchmark suites, and
source code of AutoDroid will be released at https://autodroid-sys.github.io/.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<!-- Video demo-->
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<section class="hero is-small" id="videoSection">
<div class="hero-body">
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<!-- Paper video. -->
<h2 class="title is-3">Video Demo</h2>
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<div class="publication-video" style="width: 100%;">
<!-- Video demo embed code here -->
<video class="video" controls="" autoplay="" playsinline="" muted="" loop="" style="margin-left: -70px">
<source src="./static/videos/Send message 4x.mp4" type="video/mp4" >
</video>
<video class="video" controls="" autoplay="" playsinline="" muted="" loop="" style="margin-left: 10px">
<source src="./static/videos/Create contact 8x.mp4" type="video/mp4">
</video>
<video class="video" controls="" autoplay="" playsinline="" muted="" loop="" style="margin-left: 10px">
<source src="./static/videos/Create event in calendar 5x.mp4" type="video/mp4">
</video>
<video class="video" controls="" autoplay="" playsinline="" muted="" loop="" style="margin-left: 10px">
<source src="./static/videos/Delete photo 4x.mp4" type="video/mp4">
</video>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- End Video demo -->
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<script>
// Get the <section> element and <video> elements
const section = document.getElementById('videoSection');
const videos = document.querySelectorAll('.video');
// Listen for window resize events to dynamically adjust video height
function adjustVideoHeight() {
const sectionHeight = section.clientHeight; // Get the height of the <section> element
// Set the video height to match the <section> height
videos.forEach(video => {
video.style.height = sectionHeight * 0.7 + 'px';
});
}
// Adjust video height on page load and window resize
window.addEventListener('load', adjustVideoHeight);
window.addEventListener('resize', adjustVideoHeight);
</script>
<!-- End Adjust -->
<!-- An example of AutoDroid -->
<!-- <section class="hero is-small is-light">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">An example of AutoDroid</h2>
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<img
src="static/images/intro.png"
alt="MY ALT TEXT">
<h2 class="subtitle has-text-justified">
Mem: Memory that contains app-specific knowledge automatically obtained by AutoDroid. At
runtime, AutoDroid generates actions to complete user tasks with the help of Large Language Model (LLM) augmented by the
memory.
</h2>
</div>
</div>
</div>
</div>
</section> -->
<!-- End An example of AutoDroid -->
<!-- Prompt Engineering -->
<!-- <section class="hero is-small">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Prompt Engineering</h2>
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<img src="static/images/prompt.png" alt="MY ALT TEXT">
<h2 class="subtitle has-text-justified">
An illustration of the prompt used by AutoDroid. The content in blue, red, and green boxes are the overall guidance, the
task representation, and the output requirements respectively.
</h2>
</div>
</div>
</div>
</div>
</section> -->
<!-- End Prompt Engineering -->
<!-- sec3 -->
<section class="hero is-small is-light">
<div class="hero-body">
<div class="container">
<!-- Paper video. -->
<h2 class="title is-3">Experiment Results</h2>
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<img
src="./static/images/result1.png"
alt="MY ALT TEXT">
<h2 class="subtitle has-text-justified">
The action accuracy of AutoDroid and baselines on DroidTask.
</h2>
<!-- <img
src="./static/images/result2.png"
alt="MY ALT TEXT">
<h2 class="subtitle has-text-justified">
The action accuracy of AutoDroid and LLM agent on different kinds of actions.
</h2> -->
</div>
</div>
</div>
</div>
</section>
<!-- End sec3 -->
<!-- Image carousel -->
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<div id="results-carousel" class="carousel results-carousel">
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<h2 class="subtitle has-text-centered">
First image description.
</h2>
</div>
<div class="item">
<img src="static/images/carousel2.jpg" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Second image description.
</h2>
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<h2 class="subtitle has-text-centered">
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</h2>
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<h2 class="subtitle has-text-centered">
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</h2>
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type="video/mp4">
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<video poster="" id="video3" autoplay controls muted loop height="100%">\
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<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@article{wen2023empowering,
title={Empowering llm to use smartphone for intelligent task automation},
author={Wen, Hao and Li, Yuanchun and Liu, Guohong and Zhao, Shanhui and Yu, Tao and Li, Toby Jia-Jun and Jiang, Shiqi and Liu, Yunhao and Zhang, Yaqin and Liu, Yunxin},
journal={arXiv preprint arXiv:2308.15272},
year={2023}
}</code></pre>
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