🧭 NavOL artifacts

This dataset repository contains the model checkpoints, Dingo robot asset, processed 50-scene training asset, and benchmark data used by NavOL. Source code and executable data tools are maintained at https://github.com/WAboutMe/NavOL.

🧠 Checkpoints

File	Intended use
models/navdp-cross-modal.ckpt	NavDP initialization checkpoint required to start NavOL training
models/checkpoints/navol-mpc-iter1000.pt	Default NavOL inference and benchmark evaluation
models/checkpoints/navol-mpc-iter500.pt	MPC checkpoint retained for experiment reproduction
models/checkpoints/navol-nompc-iter200.pt	Non-MPC checkpoint retained for experiment reproduction
models/checkpoints/navol-rollout128-iter100.pt	128-step rollout checkpoint

The filenames retain their training mode and iteration and should not be treated as interchangeable copies of one generic checkpoint.

🏋️ Processed training data

The canonical processed training asset is stored at:

datasets/train/3d_front_scene_50/
├── index.json
├── selected.json
├── scene.glb
├── usd/
│   ├── config.yaml
│   ├── scene.usd
│   └── textures/
└── navmesh_scenes/scene_*.glb

selected.json records the 50-scene selection. The merged scene.usd and its textures are loaded by Isaac Sim, while the 50 per-scene GLBs under navmesh_scenes/ are used by Habitat-Sim planning. scene.glb is retained as a portable reconstruction source, and usd/config.yaml is an informational, path-sanitized conversion record rather than a training input.

The training asset intentionally does not include sample_100.npy. Canonical random training uses sample_from_npy=False; a fixed reset array is needed only when that option is explicitly enabled.

🤖 Robot asset

robots/dingo.usd is the Dingo robot asset loaded by both the training and evaluation environments. Downloading it with --local-dir assets places it at the path resolved by NavOL without additional conversion.

🗺️ Benchmark data

Split	Processed archive	Raw archive
in-domain	data/benchmarks/processed/navol_benchmark_in_domain.zip	data/benchmarks/raw/raw_scenes_in_domain.zip
out-of-domain	data/benchmarks/processed/navol_benchmark_out_domain.zip	data/benchmarks/raw/raw_scenes_out_domain.zip

Each processed split contains eight scenes. Every scene includes visual GLB, simulator USD, navigation mesh, textures, and 100 fixed start-goal tasks. Use the processed archives for evaluation; use the raw archives to inspect or rebuild the scene-processing pipeline.

See BENCHMARK.md or the Chinese guide for extraction, validation, evaluation, and instructions for rebuilding an incompatible USD from its included GLB.

📥 Download

Install the Hugging Face CLI and authenticate while this repository is private or gated. Authentication is not required after it becomes public:

python -m pip install -U huggingface_hub
hf auth login

Download the initialization checkpoint, trained NavOL checkpoints, and Dingo robot asset into a NavOL checkout:

hf download WAboutme/NavOL \
  --repo-type dataset \
  --include "models/navdp-cross-modal.ckpt" \
  --include "models/checkpoints/*" \
  --include "robots/dingo.usd" \
  --local-dir assets

Download the processed training asset into a NavOL asset root:

hf download WAboutme/NavOL \
  --repo-type dataset \
  --include "datasets/train/3d_front_scene_50/**" \
  --local-dir assets

Download the benchmark archives:

hf download WAboutme/NavOL \
  --repo-type dataset \
  --include "data/benchmarks/processed/*" \
  --include "data/benchmarks/raw/*" \
  --local-dir downloads/navol

The default policy, initialization checkpoint, robot, and training data resolve to:

assets/models/checkpoints/navol-mpc-iter1000.pt
assets/models/navdp-cross-modal.ckpt
assets/robots/dingo.usd
assets/datasets/train/3d_front_scene_50/

📄 License

NavOL-authored source and configuration files use the BSD 3-Clause License. Checkpoints, training and benchmark scenes, textures, robot assets, and other third-party-derived artifacts retain the terms of their respective upstream sources. The dataset repository therefore uses license: other. See LICENSE.md and THIRD_PARTY_NOTICES.md.