README.md

---
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.