Datasets:
metadata
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.
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
- Rotation-safe mask: erode free space with circular kernel of diameter sqrt(L^2+W^2)
- Translation-safe masks: erode with oriented rectangular footprint per direction
- Reverse BFS flood-fill: seed rotation+translation-safe pixels, propagate backwards
Loading example
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.