| --- |
| language: |
| - en |
| license: cc-by-nc-4.0 |
| tags: |
| - vision-language |
| - spatial-reasoning |
| - 3d-navigation |
| - multi-agent |
| datasets: |
| - ai2thor |
| - carla |
| - procthor |
| - virtualhome |
| - EmbodiedCity |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # SpatialWorld Benchmark |
|
|
| **A Multi-Platform Benchmark for Spatial Reasoning and Spatial Task Execution** |
|
|
| ## 🎯 Overview |
|
|
| SpatialWorld is a comprehensive benchmark designed to evaluate spatial reasoning and spatial task execution capabilities of Multi-modal Large Language Models (MLLMs) and Vision-Language Models (VLMs). The benchmark spans multiple simulation platforms and diverse task categories. |
|
|
| ### Key Features |
|
|
| - **Multi-Platform Coverage**: AI2Thor, CARLA, ProcTHOR, VirtualHome, EmbodiedCity |
| - **Diverse Tasks**: Navigation, object manipulation, multi-agent coordination, and spatial reasoning |
| - **Unified Action Space**: Consistent action representation across all platforms |
| - **Rich Annotations**: Golden actions and success conditions for reproducible evaluation |
|
|
| ## 📊 Dataset Statistics |
|
|
| This Hugging Face repository contains the **SpatialWorld Benchmark** with 588 tasks in the unified dataset: |
|
|
| | Platform | Unified Tasks | Full Dataset (benchmark.zip) | Task Types | Unique Fields | |
| |----------|---------------|------------------------------|------------|---------------| |
| | AI2Thor | 343 | 2,500+ | Object manipulation, navigation | Category, Evaluation_Type, Level | |
| | CARLA | 80 | 80 | Urban navigation, traffic scenarios | executor, image_url, input_modality, origin_location, weather | |
| | ProcTHOR | 127 | 127 | Indoor navigation, household tasks | scene_index, Category, Evaluation_Type, Level | |
| | VirtualHome | 38 | 38 | Multi-agent household activities | executor, image_url, input_modality, origin_location, weather | |
| | **Total** | **588** | **~2,745** | Multi-platform spatial reasoning | **20 fields total** | |
| |
| ## 🗂️ Repository Structure |
| |
| ``` |
| spatialworld-test.jsonl # Unified dataset (588 tasks, 20 columns) |
| benchmark.zip # Full original dataset (8695 files, ~1GB) |
| README.md # This file |
| ``` |
| |
| Two versions of the data are provided: |
| |
| 1. **`spatialworld-test.jsonl`** (Unified): All 588 tasks in a unified schema with 20 columns. HF Dataset Viewer parses this file. |
| - Schema differences handled by including all possible fields |
| - Missing fields use `null` |
| - Complex nested structures encoded as JSON strings |
| - Actions converted to Unified Action Space format |
| |
| 2. **`benchmark.zip`** (Full Original Dataset): Complete raw task.json files (~1GB, 8695 files). |
| - Download and unzip to get the original folder structure: `benchmark/ai2thor/`, `benchmark/carla/`, `benchmark/procthor/`, `benchmark/virtualhome/` |
| - Use this if you need the original JSON format with platform-specific structures |
| |
| ## 🎮 Unified Action Space |
| |
| All tasks use a standardized action space: |
| |
| ### Navigation |
| - `Move(direction, distance)` - direction: forward/backward/left/right |
| |
| ### Viewpoint & Posture |
| - `Rotate(direction, angle)` - direction: left/right |
| - `Tilt(direction, angle)` - direction: up/down |
| - `ChangePosture(pose)` - standing/sitting/lying |
| |
| ### Interaction |
| - `Pick(object)` - Pick up an object |
| - `Place(target)` - Place held object at target |
| - `ChangeState(object, state)` - Toggle object state (on/off) |
| - `Manipulate(object, action)` - Complex manipulation (open/close/clean/slice) |
| |
| ### Task Control |
| - `EndTask(status)` - Terminate task (success/stopped) |
| - `Communicate(message)` - Agent-to-agent communication |
| |
| ## 📁 Task Format |
| |
| Each task contains: |
| |
| ```json |
| { |
| "task_id": "ai2thor00000", |
| "task_name": "Place object in target", |
| "instruction": "Natural language task description", |
| "scene": "FloorPlan17", |
| "golden_actions": { |
| "steps": 10, |
| "actions": [ |
| "Move(forward, 1.0)", |
| "Rotate(right, 90)", |
| "Pick(Object)", |
| "Place(Target)", |
| "EndTask(success)" |
| ] |
| }, |
| "success_conditions": [...], |
| "max_steps": 50 |
| } |
| ``` |
| |
| ## 🔧 Usage |
|
|
| ### Loading from Hugging Face |
|
|
| ```python |
| from datasets import load_dataset |
| import json |
| |
| # Load the full dataset (all 630 tasks, all 20 fields) |
| dataset = load_dataset("Spatialworld/Spatialworld-bench", split="train") |
| |
| # Access a task - all fields are available |
| task = dataset[0] |
| print(f"Task: {task['task_id']} ({task['platform']})") |
| print(f"Instruction: {task['instruction']}") |
| print(f"Scene: {task['scene']}") |
| |
| # Parse JSON-encoded fields |
| golden_actions = json.loads(task["golden_actions"]) |
| success_conditions = json.loads(task["success_conditions"]) |
| target_objects = json.loads(task["target_object_types"]) |
| |
| # Platform-specific fields (null if not applicable) |
| print(f"Category: {task['Category']}") # Only for ai2thor/procthor |
| print(f"Executor: {task['executor']}") # Only for carla/virtualhome |
| ``` |
|
|
| ### Dataset Schema |
|
|
| | Field | Type | Description | Platforms | |
| |-------|------|-------------|-----------| |
| | `task_id` | string | Unique identifier | All | |
| | `task_name` | string | Human-readable name | All | |
| | `platform` | string | Platform name | All | |
| | `instruction` | string | Natural language instruction | All | |
| | `scene` | string | Scene identifier | ai2thor, carla, virtualhome | |
| | `max_steps` | int | Maximum steps | All | |
| | `golden_actions` | string (JSON) | Action sequence | All | |
| | `success_conditions` | string (JSON) | Success criteria | All | |
| | `target_object_types` | string (JSON) | Target objects | ai2thor, procthor | |
| | `success_logic` | string | AND/OR logic | ai2thor, procthor | |
| | `target_description` | string | Detailed description | ai2thor, procthor | |
| | `Category` | string | Task category | ai2thor, procthor | |
| | `Evaluation_Type` | string | Evaluation type | ai2thor, procthor | |
| | `Level` | string | Difficulty level | ai2thor, procthor | |
| | `executor` | string | Executor type | carla, virtualhome | |
| | `image_url` | string | Image path | carla, virtualhome | |
| | `input_modality` | string | Input type | carla, virtualhome | |
| | `origin_location` | bool | Origin flag | carla, virtualhome | |
| | `scene_index` | int | Scene number | procthor | |
| | `weather` | string | Weather condition | carla, virtualhome | |
|
|
| ### Loading Original Tasks (Local) |
|
|
| For full benchmark with original JSON structures: |
|
|
| ```python |
| import json |
| from pathlib import Path |
| |
| def load_task(platform, task_id): |
| task_path = Path(f"benchmark/{platform}/tasks/{task_id}/task.json") |
| with open(task_path, 'r') as f: |
| return json.load(f) |
| |
| # Example |
| task = load_task("ai2thor", "ai2thor00000") |
| print(task["instruction"]) |
| print(task["golden_actions"]["actions"]) |
| ``` |
|
|
| ### Data Format |
|
|
| The Hugging Face dataset provides a unified schema across all platforms: |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `task_id` | string | Unique identifier (e.g., "ai2thor00000") | |
| | `platform` | string | Platform name (ai2thor/carla/procthor/virtualhome) | |
| | `task_name` | string | Human-readable task name | |
| | `instruction` | string | Natural language instruction | |
| | `scene` | string | Scene identifier (FloorPlan, Town, etc.) | |
| | `max_steps` | int | Maximum allowed steps | |
| | `golden_actions_json` | string | JSON-encoded golden action sequence | |
| | `success_conditions_json` | string | JSON-encoded success conditions | |
| | `target_object_types_json` | string | JSON-encoded target objects | |
| | `success_logic` | string | Logic for combining success conditions (AND/OR) | |
| | `target_description` | string | Detailed target description | |
| | `platform_specific_json` | string | Platform-specific fields (scene_index, executor, etc.) | |
| |
| ### Action Parsing |
| |
| ```python |
| import re |
| |
| def parse_action(action_str): |
| """Parse action string to (action_name, args)""" |
| match = re.match(r'^(\w+)\(([^)]*)\)$', action_str) |
| if match: |
| name = match.group(1) |
| args = [arg.strip() for arg in match.group(2).split(',')] |
| return name, args |
| return None, None |
| |
| # Example |
| action = "Move(forward, 1.0)" |
| name, args = parse_action(action) |
| # name: "Move", args: ["forward", "1.0"] |
| ``` |
| |
| ## 📏 Evaluation |
| |
| Tasks are evaluated based on: |
| |
| 1. **Success Rate**: Percentage of tasks completed successfully |
| 2. **Action Efficiency**: Steps used vs. golden actions |
| 3. **Goal Achievement**: Satisfaction of success conditions |
| |
| ### Success Conditions |
| |
| - `object_state`: Target object in desired state |
| - `object_in_receptacle`: Object placed in correct container |
| - `polygon_area`: Agent reached target location |
| - `agent_near_object`: Agent within distance of target |
|
|
| ## 📜 License |
|
|
| This dataset is released under CC BY-NC 4.0 (Creative Commons Attribution-NonCommercial 4.0 International). |
|
|
|
|
| --- |
|
|
| **Note**: This is an anonymized version of the dataset prepared for peer review. |
