--- license: apache-2.0 size_categories: - 1K The LearnAct Framework and LearnGUI Benchmark focus on addressing the long-tail challenges in mobile GUI agent performance through demonstration-based learning. [📄 Paper](https://arxiv.org/abs/2504.13805) | [💻 Code](https://github.com/lgy0404/LearnAct-codebase) | [🌐 Project Page](https://lgy0404.github.io/LearnAct/) ## Overview LearnGUI is the first comprehensive dataset specifically designed for studying demonstration-based learning in mobile GUI agents. It comprises 2,353 instructions across 73 applications with an average of 13.2 steps per task, featuring high-quality human demonstrations for both offline and online evaluation scenarios. ## 🌟 Key Features - **Unified Benchmark Framework**: Provides standardized metrics and evaluation protocols for demonstration-based learning in mobile GUI agents - **Dual Evaluation Modes**: Supports both offline (2,252 tasks) and online (101 tasks) evaluation scenarios to assess agent performance - **Rich Few-shot Learning Support**: Includes k-shot combinations (k=1,2,3) for each task with varying similarity profiles - **Multi-dimensional Similarity Metrics**: Quantifies demonstration relevance across instruction, UI, and action dimensions - **Diverse Real-world Coverage**: Spans 73 mobile applications with 2,353 naturally varied tasks reflecting real-world usage patterns - **Expert-annotated Trajectories**: Contains high-quality human demonstrations with detailed step-by-step action sequences and element annotations ## 📊 Dataset Structure and Statistics The dataset is organized into three main splits: ### Dataset Statistics | Split | K-shot | Tasks | Apps | Step actions | Avg InsSim | Avg UISim | Avg ActSim | UISHActSH | UISHActSL | UISLActSH | UISLActSL | |-------|--------|-------|------|-------------|------------------------|----------------------|----------------------|--------------------------------|--------------------------------|--------------------------------|--------------------------------| | Offline-Train | 1-shot | 2,001 | 44 | 26,184 | 0.845 | 0.901 | 0.858 | 364 | 400 | 403 | 834 | | Offline-Train | 2-shot | 2,001 | 44 | 26,184 | 0.818 | 0.898 | 0.845 | 216 | 360 | 358 | 1,067 | | Offline-Train | 3-shot | 2,001 | 44 | 26,184 | 0.798 | 0.895 | 0.836 | 152 | 346 | 310 | 1,193 | | Offline-Test | 1-shot | 251 | 9 | 3,469 | 0.798 | 0.868 | 0.867 | 37 | 49 | 56 | 109 | | Offline-Test | 2-shot | 251 | 9 | 3,469 | 0.767 | 0.855 | 0.853 | 15 | 42 | 55 | 139 | | Offline-Test | 3-shot | 251 | 9 | 3,469 | 0.745 | 0.847 | 0.847 | 10 | 36 | 49 | 156 | | Online-Test | 1-shot | 101 | 20 | 1,423 | - | - | - | - | - | - | - | Each task in LearnGUI contains: - High-level instruction - Low-level action sequences - Screenshot of each step - UI element details - Ground truth action labels - Demonstration pairings with varying similarity profiles ## 📁 Directory Structure ``` LearnGUI/ ├── offline/ # Offline evaluation dataset │ ├── screenshot.zip # Screenshot archives (multi-part) │ ├── screenshot.z01-z05 # Screenshot archive parts │ ├── element_anno.zip # Element annotations │ ├── instruction_anno.zip # Instruction annotations │ ├── task_spilit.json # Task splitting information │ └── low_level_instructions.json # Detailed step-by-step instructions │ ├── online/ # Online evaluation dataset │ ├── low_level_instructions/ # JSON files with step instructions for each task │ │ ├── AudioRecorderRecordAudio.json │ │ ├── BrowserDraw.json │ │ ├── SimpleCalendarAddOneEvent.json │ │ └── ... (98 more task instruction files) │ └── raw_data/ # Raw data for each online task │ ├── AudioRecorderRecordAudio/ │ ├── BrowserDraw/ │ ├── SimpleCalendarAddOneEvent/ │ └── ... (98 more task data directories) │ └── static/ # Website assets and images └── images/ # Dataset visualization images ``` ## 🔍 Comparison with Existing Datasets LearnGUI offers several advantages over existing GUI datasets: | Dataset | # Inst. | # Apps | # Step | Env. | HL | LL | GT | FS | |---------|---------|--------|--------|------|----|----|----|----| | PixelHelp | 187 | 4 | 4.2 | ❌ | ✅ | ❌ | ✅ | ❌ | | MoTIF | 276 | 125 | 4.5 | ❌ | ✅ | ✅ | ✅ | ❌ | | UIBert | 16,660 | - | 1 | ❌ | ❌ | ✅ | ✅ | ❌ | | UGIF | 523 | 12 | 6.3 | ❌ | ✅ | ✅ | ✅ | ❌ | | AITW | 30,378 | 357 | 6.5 | ❌ | ✅ | ❌ | ✅ | ❌ | | AITZ | 2,504 | 70 | 7.5 | ❌ | ✅ | ✅ | ✅ | ❌ | | AndroidControl | 15,283 | 833 | 4.8 | ❌ | ✅ | ✅ | ✅ | ❌ | | AMEX | 2,946 | 110 | 12.8 | ❌ | ✅ | ❌ | ✅ | ❌ | | MobileAgentBench | 100 | 10 | - | ❌ | ✅ | ❌ | ❌ | ❌ | | AppAgent | 50 | 10 | - | ❌ | ✅ | ❌ | ❌ | ❌ | | LlamaTouch | 496 | 57 | 7.01 | ✅ | ✅ | ❌ | ✅ | ❌ | | AndroidWorld | 116 | 20 | - | ✅ | ✅ | ❌ | ❌ | ❌ | | AndroidLab | 138 | 9 | 8.5 | ✅ | ✅ | ❌ | ❌ | ❌ | | **LearnGUI (Ours)** | **2,353** | **73** | **13.2** | ✅ | ✅ | ✅ | ✅ | ✅ | *Note: # Inst. (number of instructions), # Apps (number of applications), # Step (average steps per task), Env. (supports environment interactions), HL (has high-level instructions), LL (has low-level instructions), GT (provides ground truth trajectories), FS (supports few-shot learning).* ## 📄 License This dataset is licensed under Apache License 2.0.