| --- |
| license: mit |
| tags: |
| - robotics |
| - occupancy-grid |
| - motion-planning |
| - topological-traps |
| - floor-plans |
| - houseexpo |
| task_categories: |
| - image-segmentation |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # Topological Traps Dataset |
|
|
| **TAU Algorithmic Robotics - Fall 2025/2026 - Daniel Simanovsky** |
|
|
| Pre-processed occupancy grids and Oracle viability labels derived from the |
| HouseExpo residential floor-plan dataset, used to train |
| [DanielDDDs/topological-traps](https://huggingface.co/DanielDDDs/topological-traps). |
|
|
| ## Contents |
|
|
| | Path | Description | Size | |
| |---|---|---| |
| | `data/processed/` | 1,001 binary occupancy grids (512x512 px, .npy) | ~262 MB | |
| | `data/manifest.csv` | Train/val/test split (700/150/150), seed 42 | <1 MB | |
| | `data/labels/robot_20x15/` | Oracle viability labels, small robot | ~1 GB | |
| | `data/labels/robot_30x20/` | Oracle viability labels, default robot | ~1 GB | |
| | `data/labels/robot_40x25/` | Oracle viability labels, large robot | ~1 GB | |
| | `data/labels/robot_25x18/` | Oracle viability labels, unseen test robot | ~1 GB | |
|
|
| ## Label format |
|
|
| Each label file is a (4, 512, 512) uint8 NumPy array. |
| Channel order: [North, South, East, West]. |
| 1 = viable (robot can escape), 0 = directional trap. |
|
|
| ## Robot sizes |
|
|
| | Size (LxW px) | Diagonal (px) | Split | |
| |---|---|---| |
| | 20x15 | 25 | Train | |
| | 30x20 | 36 | Train | |
| | 40x25 | 47 | Train | |
| | 25x18 | 31 | Test only (unseen) | |
|
|
| ## Oracle algorithm |
|
|
| 1. Rotation-safe mask: erode free space with circular kernel of diameter sqrt(L^2+W^2) |
| 2. Translation-safe masks: erode with oriented rectangular footprint per direction |
| 3. Reverse BFS flood-fill: seed rotation+translation-safe pixels, propagate backwards |
|
|
| ## Loading example |
|
|
| ```python |
| import numpy as np |
| |
| occ = np.load("data/processed/0041a20dcdfd5e0d1ca0752365a70634.npy") |
| # shape (512, 512), uint8, 1=free 0=wall |
| |
| label = np.load("data/labels/robot_30x20/0041a20dcdfd5e0d1ca0752365a70634.npy") |
| # shape (4, 512, 512), uint8 |
| north_viable = label[0] |
| ``` |
|
|
| ## Source |
|
|
| 1,000 maps from HouseExpo (Li et al., 2019, arXiv:1903.09845), rasterised to 512x512. |
|
|
| ## Code |
|
|
| [github.com/danielsddd/topological-traps-project](https://github.com/danielsddd/topological-traps-project) |
|
|
