Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception
- URL: http://arxiv.org/abs/2401.16158v2
- Date: Thu, 18 Apr 2024 06:53:38 GMT
- Title: Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception
- Authors: Junyang Wang, Haiyang Xu, Jiabo Ye, Ming Yan, Weizhou Shen, Ji Zhang, Fei Huang, Jitao Sang,
- Abstract summary: We introduce Mobile-Agent, an autonomous multi-modal mobile device agent.
Mobile-Agent first leverages visual perception tools to accurately identify and locate both the visual and textual elements within the app's front-end interface.
It then autonomously plans and decomposes the complex operation task, and navigates the mobile Apps through operations step by step.
- Score: 52.5831204440714
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Mobile device agent based on Multimodal Large Language Models (MLLM) is becoming a popular application. In this paper, we introduce Mobile-Agent, an autonomous multi-modal mobile device agent. Mobile-Agent first leverages visual perception tools to accurately identify and locate both the visual and textual elements within the app's front-end interface. Based on the perceived vision context, it then autonomously plans and decomposes the complex operation task, and navigates the mobile Apps through operations step by step. Different from previous solutions that rely on XML files of Apps or mobile system metadata, Mobile-Agent allows for greater adaptability across diverse mobile operating environments in a vision-centric way, thereby eliminating the necessity for system-specific customizations. To assess the performance of Mobile-Agent, we introduced Mobile-Eval, a benchmark for evaluating mobile device operations. Based on Mobile-Eval, we conducted a comprehensive evaluation of Mobile-Agent. The experimental results indicate that Mobile-Agent achieved remarkable accuracy and completion rates. Even with challenging instructions, such as multi-app operations, Mobile-Agent can still complete the requirements. Code and model will be open-sourced at https://github.com/X-PLUG/MobileAgent.
Related papers
- Foundations and Recent Trends in Multimodal Mobile Agents: A Survey [57.677161006710065]
Mobile agents are essential for automating tasks in complex and dynamic mobile environments.
Recent advancements enhance real-time adaptability and multimodal interaction.
We categorize these advancements into two main approaches: prompt-based methods and training-based methods.
arXiv Detail & Related papers (2024-11-04T11:50:58Z) - SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation [89.24729958546168]
We present SPA-Bench, a comprehensive SmartPhone Agent Benchmark designed to evaluate (M)LLM-based agents.
SPA-Bench offers three key contributions: A diverse set of tasks covering system and third-party apps in both English and Chinese, focusing on features commonly used in daily routines.
A novel evaluation pipeline that automatically assesses agent performance across multiple dimensions, encompassing seven metrics related to task completion and resource consumption.
arXiv Detail & Related papers (2024-10-19T17:28:48Z) - AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents [52.13695464678006]
This study enhances an LLM-based web agent by simply refining its observation and action space.
AgentOccam surpasses the previous state-of-the-art and concurrent work by 9.8 (+29.4%) and 5.9 (+15.8%) absolute points respectively.
arXiv Detail & Related papers (2024-10-17T17:50:38Z) - AppAgent v2: Advanced Agent for Flexible Mobile Interactions [46.789563920416626]
This work introduces a novel LLM-based multimodal agent framework for mobile devices.
Our agent constructs a flexible action space that enhances adaptability across various applications.
Our results demonstrate the framework's superior performance, confirming its effectiveness in real-world scenarios.
arXiv Detail & Related papers (2024-08-05T06:31:39Z) - MobileAgentBench: An Efficient and User-Friendly Benchmark for Mobile LLM Agents [7.4568642040547894]
Large language model (LLM)-based mobile agents are increasingly popular due to their capability to interact directly with mobile phone Graphic User Interfaces (GUIs)
Despite their promising prospects in both academic and industrial sectors, little research has focused on benchmarking the performance of existing mobile agents.
We propose an efficient and user-friendly benchmark, MobileAgentBench, designed to alleviate the burden of extensive manual testing.
arXiv Detail & Related papers (2024-06-12T13:14:50Z) - Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration [52.25473993987409]
We propose Mobile-Agent-v2, a multi-agent architecture for mobile device operation assistance.
The architecture comprises three agents: planning agent, decision agent, and reflection agent.
We show that Mobile-Agent-v2 achieves over a 30% improvement in task completion compared to the single-agent architecture.
arXiv Detail & Related papers (2024-06-03T05:50:00Z) - Benchmarking Mobile Device Control Agents across Diverse Configurations [19.01954948183538]
B-MoCA is a benchmark for evaluating and developing mobile device control agents.
We benchmark diverse agents, including agents employing large language models (LLMs) or multi-modal LLMs.
While these agents demonstrate proficiency in executing straightforward tasks, their poor performance on complex tasks highlights significant opportunities for future research to improve effectiveness.
arXiv Detail & Related papers (2024-04-25T14:56:32Z) - MobileAgent: enhancing mobile control via human-machine interaction and
SOP integration [0.0]
Large Language Models (LLMs) are now capable of automating mobile device operations for users.
Privacy concerns related to personalized user data arise during mobile operations, requiring user confirmation.
We have designed interactive tasks between agents and humans to identify sensitive information and align with personalized user needs.
Our approach is evaluated on the new device control benchmark AitW, which encompasses 30K unique instructions across multi-step tasks.
arXiv Detail & Related papers (2024-01-04T03:44:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.