Generates synthetic datasets for training and evaluating vision models on grid navigation and path planning tasks. Each sample contains a grid with a start point, end point, and scattered reward items, requiring the agent to find the shortest path that collects all rewards before reaching the end.
Each sample pairs a task (first frame + prompt describing what needs to happen) with its ground truth solution (final frame showing the result + video demonstrating how to achieve it). This structure enables both model evaluation and training.
| Property | Value |
|---|---|
| Task ID | G-12 |
| Task | Grid Obtaining Award |
| Category | Spatiality |
| Resolution | 1024×1024 px |
| FPS | 16 fps |
| Duration | ~6 seconds |
| Output | PNG images + MP4 video |
# 1. Clone the repository
git clone https://github.com/VBVR-DataFactory/G-12_grid_obtaining_award_data-generator.git
cd G-12_grid_obtaining_award_data-generator
# 2. Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# 3. Install dependencies
pip install --upgrade pip
pip install -r requirements.txt
pip install -e .# Generate 50 samples
python examples/generate.py --num-samples 50
# Custom output directory
python examples/generate.py --num-samples 100 --output data/my_dataset
# Reproducible generation with seed
python examples/generate.py --num-samples 50 --seed 42
# Without videos (faster)
python examples/generate.py --num-samples 50 --no-videos| Argument | Description |
|---|---|
--num-samples |
Number of tasks to generate (required) |
--output |
Output directory (default: data/questions) |
--seed |
Random seed for reproducibility |
--no-videos |
Skip video generation (images only) |
The scene shows a 10x10 grid with a green start point, a red end point, and 3 triangle reward items scattered across it. A circular agent starts at the green start point and can move to adjacent cells (up, down, left, right). The agent collects a reward by moving to its cell, and once collected, the reward disappears. Find the shortest path that collects all 3 triangle rewards before reaching the red end point.
 |
 |
 |
| Initial Frame Agent at start, rewards scattered |
Animation Agent collects rewards via shortest path |
Final Frame All rewards collected, agent at end |
Navigate a grid to collect all reward items using the shortest path, then reach the end point.
- Grid: 10×10 grid of cells
- Start point: Green filled cell (agent starts here)
- End point: Red filled cell (final destination)
- Rewards: 3-4 reward items (diamond, circle, triangle, or star shapes) with 30 different colors, scattered across grid
- Each reward has a unique ID, position, and color
- Rewards are color-filled (not just outlines)
- Agent: Orange circular character
- Movement: Can move up, down, left, right to adjacent cells
- Background: White grid with black borders
- Goal: Collect all rewards via shortest path before reaching end
- Rewards disappear when collected (visual feedback synchronized with agent arrival)
- Path must visit all reward cells before end cell
- Shortest path optimization required (TSP-like problem)
- Grid-based movement (no diagonal movement)
- Clear color coding (green=start, red=end, 30-color palette for rewards, orange=agent)
- Smooth continuous movement (agent moves pixel-by-pixel between cells, not jumping)
- Each reward has metadata: ID, position, color, and collection order
data/questions/grid_obtaining_award_task/grid_obtaining_award_00000000/
├── first_frame.png # Initial grid with agent at start
├── final_frame.png # Agent at end, all rewards collected
├── prompt.txt # Navigation task instruction
├── ground_truth.mp4 # Animation of optimal path
└── question_metadata.json # Task metadata
File specifications:
- Images: 1024×1024 PNG format
- Video: MP4 format, 16 fps, ~6 seconds duration
spatiality path-planning grid-navigation optimization reward-collection