MobA: Multifaceted Memory-Enhanced Adaptive Planning for Efficient Mobile Task Automation

• URL: http://arxiv.org/abs/2410.13757v2
• Date: Sun, 02 Mar 2025 07:34:35 GMT
• Title: MobA: Multifaceted Memory-Enhanced Adaptive Planning for Efficient Mobile Task Automation
• Authors: Zichen Zhu, Hao Tang, Yansi Li, Dingye Liu, Hongshen Xu, Kunyao Lan, Danyang Zhang, Yixuan Jiang, Hao Zhou, Chenrun Wang, Situo Zhang, Liangtai Sun, Yixiao Wang, Yuheng Sun, Lu Chen, Kai Yu,
• Abstract summary: We propose MobA, a novel MLLM-based mobile assistant system.<n>A multifaceted memory module provides comprehensive memory support to enhance adaptability and efficiency.<n> Experimental results on MobBench and AndroidArena demonstrate MobA's ability to handle dynamic GUI environments and perform complex mobile task.
• Score: 23.026244256950086
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Existing Multimodal Large Language Model (MLLM)-based agents face significant challenges in handling complex GUI (Graphical User Interface) interactions on devices. These challenges arise from the dynamic and structured nature of GUI environments, which integrate text, images, and spatial relationships, as well as the variability in action spaces across different pages and tasks. To address these limitations, we propose MobA, a novel MLLM-based mobile assistant system. MobA introduces an adaptive planning module that incorporates a reflection mechanism for error recovery and dynamically adjusts plans to align with the real environment contexts and action module's execution capacity. Additionally, a multifaceted memory module provides comprehensive memory support to enhance adaptability and efficiency. We also present MobBench, a dataset designed for complex mobile interactions. Experimental results on MobBench and AndroidArena demonstrate MobA's ability to handle dynamic GUI environments and perform complex mobile task.

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