Building a Stable Planner: An Extended Finite State Machine Based Planning Module for Mobile GUI Agent

• URL: http://arxiv.org/abs/2505.14141v1
• Date: Tue, 20 May 2025 09:45:55 GMT
• Title: Building a Stable Planner: An Extended Finite State Machine Based Planning Module for Mobile GUI Agent
• Authors: Fanglin Mo, Junzhe Chen, Haoxuan Zhu, Xuming Hu,
• Abstract summary: We propose SPlanner, a plug-and-play planning module to generate execution plans that guide vision language model(VLMs) in executing tasks.<n>SPlanner achieves a 63.8% task success rate when paired with Qwen2.5-VL-72B as the VLM, yielding a 28.8 percentage point improvement compared to using Qwen2.5-VL-72B without planning assistance.
• Score: 13.259836345131525
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Mobile GUI agents execute user commands by directly interacting with the graphical user interface (GUI) of mobile devices, demonstrating significant potential to enhance user convenience. However, these agents face considerable challenges in task planning, as they must continuously analyze the GUI and generate operation instructions step by step. This process often leads to difficulties in making accurate task plans, as GUI agents lack a deep understanding of how to effectively use the target applications, which can cause them to become "lost" during task execution. To address the task planning issue, we propose SPlanner, a plug-and-play planning module to generate execution plans that guide vision language model(VLMs) in executing tasks. The proposed planning module utilizes extended finite state machines (EFSMs) to model the control logits and configurations of mobile applications. It then decomposes a user instruction into a sequence of primary function modeled in EFSMs, and generate the execution path by traversing the EFSMs. We further refine the execution path into a natural language plan using an LLM. The final plan is concise and actionable, and effectively guides VLMs to generate interactive GUI actions to accomplish user tasks. SPlanner demonstrates strong performance on dynamic benchmarks reflecting real-world mobile usage. On the AndroidWorld benchmark, SPlanner achieves a 63.8% task success rate when paired with Qwen2.5-VL-72B as the VLM executor, yielding a 28.8 percentage point improvement compared to using Qwen2.5-VL-72B without planning assistance.

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