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vln-zero.github.io

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[NeuS 2026 (Spotlight)] VLN-Zero: Rapid Exploration and Cache-Enabled Neurosymbolic Vision-Language Planning for Zero-Shot Transfer in Robot Navigation

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# VLN-Zero
Rapid Exploration and Cache-Enabled Neurosymbolic Vision-Language Planning for Zero-Shot Transfer in Robot Navigation

![Framework Overview](https://vln-zero.github.io/static/images/vln-zero-framework.png)

## Installation

1. Clone this repo

```bash
git clone --recursive https://github.com/VLN-Zero/vln-zero.github.io.git
cd vln-zero.github.io
```

2. Make changes to the `habitat-lab` code

In `habitat-lab/habitat/utils/visualizations/maps.py`, change lines 425-426 to the following:

```python
    limit = topdown_map_info["limits"]
    # Crop the map to show only the agent and goal +- some buffer
    top_down_map = top_down_map[limit[0]:limit[1], limit[2]:limit[3]]
```

Also, in `habitat-lab/habitat_baselines/rl/requirements.txt`, remove lines 3-4 (the `tensorflow==1.13.1` requirement is not needed).

3. Setup your Conda environment

Use the following instructions to install all necessary dependencies.

```bash
conda create -n vln-zero python=3.8
source $(conda info --base)/etc/profile.d/conda.sh
conda activate vln-zero
wget https://anaconda.org/aihabitat/habitat-sim/0.1.7/download/linux-64/habitat-sim-0.1.7-py3.8_headless_linux_856d4b08c1a2632626bf0d205bf46471a99502b7.tar.bz2
conda install habitat-sim-0.1.7-py3.8_headless_linux_856d4b08c1a2632626bf0d205bf46471a99502b7.tar.bz2
# you can remove the tar after running this

cd habitat-lab/
# If pip's dependency resolver fails on the following instructions, run `python -m pip install --upgrade pip` and then try again.
python -m pip install -r requirements.txt
python -m pip install -r habitat_baselines/rl/requirements.txt # this should already have been modified in step 2
python -m pip install -r habitat_baselines/rl/ddppo/requirements.txt
python -m pip install -e .

cd ../
python -m pip install tokenizers==0.19.1
python -m pip install tensorboard
python -m pip install -r requirements.txt
```

4. Download data

Following the steps from the [VLN-CE](https://github.com/jacobkrantz/VLN-CE) project, download the MP3D, R2R, and RxR datasets. The final structure should look like something like this.

```
VLN-Zero
├─ habitat-lab
├─ VLN_CE
│  ├─ data
│  │  ├─ datasets
│  │  │  ├─ R2R_VLNCE_v1-3
│  │  │  │  ├─ test
│  │  │  │  ├─ train
│  │  │  │  ├─ val_seen
│  │  │  │  ├─ val_unseen
│  │  │  ├─ R2R_VLNCE_v1-3_preprocessed
│  │  │  │  ├─ envdrop
│  │  │  │  ├─ joint_train_envdrop
│  │  │  │  ├─ test
│  │  │  │  ├─ train
│  │  │  │  ├─ val_seen
│  │  │  │  ├─ val_unseen
│  │  │  ├─ RxR_VLNCE_v0
│  │  │  │  ├─ train
│  │  │  │  ├─ val_seen
│  │  │  │  ├─ val_unseen
│  │  │  │  ├─ test_challenge
│  │  │  │  ├─ text_features
│  │  ├─ ddppo-models
│  │  ├─ res
│  │  ├─ scene_datasets
│  │  │  ├─ mp3d
```

## Evaluation
To run our implementation, run ```bash eval_zero_vlnce.sh```. The number of gpus used can me modified in this script. Make sure to set the OPENAI_API_KEY variable to your key (Warning! With multiple gpus in use, this script will use a LOT of API calls).

Results can be tracked by running ```python analyze_results.py --path YOUR_PATH```

The evaluation can be killed by running ```bash kill_zero_eval.sh```

These scripts were taken from [NaVid-VLN-CE](https://github.com/jzhzhang/NaVid-VLN-CE).

## Citation
Please cite with
```
@misc{anonymous2025vlnzerorapidexplorationcacheenabled,
      title={VLN-Zero: Rapid Exploration and Cache-Enabled Neurosymbolic Vision-Language Planning for Zero-Shot Transfer in Robot Navigation}, 
      author={Anonymous},
      year={2025},
      url={https://vln-zero.github.io/},
}
```

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