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README.md

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+---
+language:
+- en
+license: cc-by-nc-4.0
+tags:
+- embodied-ai
+- vision-language
+- spatial-reasoning
+- 3d-navigation
+- multi-agent
+datasets:
+- ai2thor
+- carla
+- procthor
+- virtualhome
+- embodiedcity
+size_categories:
+- 1K<n<10K
+---
+
 # SpatialWorld Benchmark
 
-> **Anonymous Repository for NeurIPS 2026 Submission**
+**A Multi-Platform Benchmark for Spatial Reasoning and Embodied Task Execution**
 
-This is an anonymous dataset repository for peer review purposes. All identifying information has been removed to maintain double-blind review compliance.
+## 🎯 Overview
 
----
+SpatialWorld is a comprehensive benchmark designed to evaluate spatial reasoning and embodied 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.
 
-## Overview
+### Key Features
 
-**SpatialWorld** is a comprehensive benchmark for evaluating embodied AI agents in complex 3D spatial reasoning and manipulation tasks. The benchmark encompasses diverse virtual environments and task types to assess agents' capabilities in understanding and interacting with physical spaces.
+- **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
 
-## Benchmark Components
+| Platform | Scenarios | Tasks | Task Types |
+|----------|-----------|-------|------------|
+| AI2Thor | 120 | ~2,500 | Object manipulation, navigation |
+| CARLA | 5 Towns | 80 | Urban navigation, traffic scenarios |
+| ProcTHOR | Procedural | ~1,200 | Indoor navigation, household tasks |
+| VirtualHome | 7 Houses | ~800 | Multi-agent household activities |
+| EmbodiedCity | City-scale | ~400 | Real-world urban scenarios |
+| Game Tasks | Various | 100 | Rubik's cube, maze solving, etc. |
 
-### Environments
+## 🗂️ Repository Structure
 
-The benchmark includes the following simulation environments:
+```
+benchmark/
+├── ai2thor/          # Indoor household environments
+├── carla/            # Urban driving simulation
+├── procthor/         # Procedurally generated homes
+├── virtualhome/      # Multi-agent household simulation
+├── embodiedcity/     # Large-scale urban environments
+├── game/             # Specialized spatial tasks
+└── shared/           # Dual-agent coordination tasks
+```
 
-| Environment | Description | Tasks |
-|-------------|-------------|-------|
-| **AI2-THOR** | Indoor household environments | Object manipulation, navigation, and rearrangement tasks |
-| **CARLA** | Autonomous driving simulator | Driving scenarios and vehicle control tasks |
-| **VirtualHome** | Multi-agent household simulator | Multi-step task completion and social interactions |
-| **ProcTHOR** | Procedurally generated homes | Generalization across diverse household layouts |
-| **EmbodiedCity** | Urban environment simulation | City-scale navigation and exploration |
-| **Game** | Puzzle and game environments | Rubik's cube, maze solving, and 3D block puzzles |
+## 🎮 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
+}
+```
 
-### Task Structure
+## 🔧 Usage
 
-Each task in the benchmark contains:
+### Loading Tasks
 
-- **Task ID**: Unique identifier (e.g., `ai2thor00000`)
-- **Instruction**: Natural language description of the goal
-- **Scene/Environment**: The specific scene configuration
-- **Golden Actions**: Reference action sequence for task completion
-- **Success Conditions**: Formal criteria for task success evaluation
-- **Initial State**: Starting configuration via `init.json`
+```python
+import json
+from pathlib import Path
 
-### Data Format
+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"])
 ```
-benchmark/
-├── ai2thor/tasks/{task_id}/
-│   ├── task.json      # Task definition and success conditions
-│   └── init.json      # Initial scene configuration
-├── carla/tasks/{task_id}/
-│   ├── task.json
-│   └── init.json
-├── embodiedcity/
-│   └── Datasets/
-├── game/data/
-│   ├── rubik_cube/
-│   ├── maze3d/
-│   ├── maze3d_pro/
-│   └── Block3D/
-├── procthor/
-│   └── tasks/
-└── virtualhome/
-    └── tasks/
+
+### 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.