| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - robotics |
| - motion-planning |
| - segmentation |
| - unet |
| - topological-traps |
| - viability-prediction |
| - non-holonomic |
| pipeline_tag: image-segmentation |
| datasets: |
| - DanielDDDs/topological-traps-dataset |
| --- |
| |
| # Neural Prediction of Heading-Dependent Topological Traps |
|
|
| **TAU Algorithmic Robotics - Fall 2025/2026 - Daniel Simanovsky** |
|
|
| A U-Net model that predicts directional viability maps for non-holonomic robot navigation: |
| given a 2D occupancy grid and robot dimensions, the model outputs four binary masks (N/S/E/W) |
| indicating which pixels are topological traps - cells the robot can enter but cannot escape |
| by translating in that heading without first rotating. |
|
|
| ## Models in this repo |
|
|
| | File | Description | Val IoU | |
| |---|---|---| |
| | `models/viability_cardinal_best_iou.pth` | Cardinal model - 3-ch input, 4-ch binary output (N/S/E/W) | 0.9793 | |
| | `models/viability_continuous_angle_best_iou.pth` | Angle model - 5-ch input (sin/cos theta), 1-ch arbitrary heading | 0.984 | |
| | `configs/training_config.yaml` | Training configuration | - | |
|
|
| ## Architecture |
|
|
| - Backbone: U-Net with ResNet34 encoder (ImageNet pre-trained), ~24.5M parameters |
| - Input: 3-channel tensor - binary occupancy grid + normalised robot L/512, W/512 |
| - Output: 4-channel sigmoid map, one per cardinal direction |
| - Training: AdamW (lr=1e-4, wd=1e-4), CosineAnnealingLR, BCE+Dice loss, FP16, batch 16, 50 epochs |
|
|
| ## Results |
|
|
| | Metric | Value | |
| |---|---| |
| | IoU (seen sizes) | 0.978 | |
| | IoU (unseen size 25x18) | 0.953 | |
| | Generalisation gap | 0.025 | |
| | Inference time | 8.8 ms/map (GPU) | |
| | Oracle speedup | 21.5x | |
| | PRM trap reduction | 81% | |
|
|
| ## Quick start |
|
|
| ```python |
| import torch, numpy as np |
| from huggingface_hub import hf_hub_download |
| import segmentation_models_pytorch as smp |
| |
| ckpt_path = hf_hub_download( |
| repo_id="DanielDDDs/topological-traps", |
| filename="models/viability_cardinal_best_iou.pth" |
| ) |
| model = smp.Unet(encoder_name="resnet34", encoder_weights=None, in_channels=3, classes=4) |
| state = torch.load(ckpt_path, map_location="cpu") |
| model.load_state_dict(state["model_state_dict"]) |
| model.eval() |
| |
| def predict(occupancy, robot_L=30, robot_W=20): |
| x = np.zeros((1, 3, 512, 512), dtype=np.float32) |
| x[0, 0] = occupancy |
| x[0, 1] = robot_L / 512.0 |
| x[0, 2] = robot_W / 512.0 |
| with torch.no_grad(): |
| out = torch.sigmoid(model(torch.from_numpy(x))) |
| return out[0].numpy() # (4, 512, 512) N/S/E/W viability |
| |
| viability = predict(occupancy_grid) |
| north_viable = viability[0] > 0.5 |
| ``` |
|
|
| ## Dataset |
|
|
| Full processed maps and Oracle labels: [DanielDDDs/topological-traps-dataset](https://huggingface.co/datasets/DanielDDDs/topological-traps-dataset) |
|
|
| ## Code |
|
|
| [github.com/danielsddd/topological-traps-project](https://github.com/danielsddd/topological-traps-project) |
|
|
| ## Compute |
|
|
| TAU CS SLURM cluster: |
| - NVIDIA TITAN Xp (12 GB) |
| - NVIDIA GeForce RTX 2080 (8 GB) |
|
|
| If you use this model in your research, please cite it as follows: |
|
|
| ```bibtex |
| @misc{simanovsky2026neural, |
| author = {Daniel Simanovsky}, |
| title = {Neural Prediction of Heading-Dependent Topological Traps}, |
| year = {2026}, |
| school = {Tel Aviv University}, |
| } |
