Initial upload: 4-cond DD-PPO PointNav checkpoints + README

README.md

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+# Pre-trained checkpoints — *How sensor structure shapes the format of spatial memory in navigation agents*
+
+DD-PPO PointNav agents trained in [Habitat](https://github.com/facebookresearch/habitat-lab) on a merged Gibson-0+ + Matterport3D-train pool (472 scenes), with one varied component: the visual sensor.
+
+The four conditions span an encoder spatial-feature spectrum:
+
+| Condition | Visual input | ResNet-18 spatial output | Frames | Final ckpt |
+|---|---|---|---|---|
+| `blind` | None | (no encoder) | 340M | `blind/ckpt.34.pth` |
+| `coarse` | 48×48 RGB (uniform) | 1×1 feature map | 250M | `coarse/ckpt.49.pth` |
+| `foveated` | 256×256 with eccentricity-dep. Gaussian blur ($\sigma_{\max}=8$, gaze fixed at image centre) | 8×8 feature map | 180M† | `foveated/ckpt.36.pth` |
+| `uniform` | 256×256 RGB (no blur) | 8×8 feature map | 250M | `uniform/ckpt.49.pth` |
+
+†`foveated` reached convergence at 180M (success 0.94, comparable to other sighted conds at 200M); training diverged with a $\sqrt{0}$-gradient instability past this point. The `ckpt.36` weights are NaN-free.
+
+## Intermediate checkpoints
+
+For reproducing the cross-training substitution figure (§4.2 of the paper), intermediate checkpoints are included:
+
+- `blind/`: `ckpt.{5, 10, 20, 25, 30, 34}.pth` (50M, 100M, 200M, 250M, 300M, 340M frames)
+- `coarse/`: `ckpt.{10, 20, 30, 40, 49}.pth` (50M, 100M, 150M, 200M, 245M frames)
+- `foveated/`: `ckpt.{10, 20, 30, 36}.pth` (50M, 100M, 150M, 180M frames)
+- `uniform/`: `ckpt.{10, 20, 30, 40, 49}.pth` (50M, 100M, 150M, 200M, 245M frames)
+
+Frame counts are recorded in each `.pth`'s `extra_state['step']` field. Use 5M frames/ckpt for sighted conditions and 10M frames/ckpt for `blind`.
+
+## Architecture
+
+All four checkpoints share an identical backbone:
+
+- **Recurrent module**: 3-layer LSTM, hidden dim 512
+- **Non-visual sensor stack**: GPS, compass, distance-to-goal, goal-in-start-frame; each projected to 32-d and concatenated with a 32-d previous-action embedding (Wijmans et al. 2023 protocol)
+- **Visual encoder** (where present): ResNet-18, output flattened
+- **Policy / value heads**: linear projections from $\mathbf{h}_2$
+- **Algorithm**: DD-PPO~\cite{wijmans2020ddppo}
+
+## Loading
+
+```python
+import torch
+from habitat_baselines import baseline_registry
+
+ckpt = torch.load("blind/ckpt.34.pth", map_location="cpu", weights_only=False)
+state_dict = ckpt["state_dict"]
+config = ckpt["config"]
+num_steps = ckpt["extra_state"]["step"]  # frames trained
+```
+
+To reconstruct the policy and run inference, see the [companion code](https://github.com/alunxu/foveated-cog-map) `scripts/probing/collect.py` for an end-to-end example.
+
+## Citation
+
+If you use these checkpoints, please cite:
+
+```bibtex
+@inproceedings{xu2026sensor,
+  title={How Sensor Structure Shapes the Format of Spatial Memory in Navigation Agents},
+  author={Xu, Weilun and ...},
+  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
+  year={2026}
+}
+```
+
+## License
+
+CC-BY-4.0. Free to use, modify, redistribute with attribution.

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