VoLN-UAV

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Official code for VoLN: Vision-Only Long-Horizon Navigation—Paradigm, Benchmark, and Method. An embodied AI UAV benchmark and VoLN-MLLM agent bridging VLN and multimodal LLMs across 7,210 episodes, AirSim, and real-world flights.

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<p><a href="https://github.com/Admire-ljb/VoLN-UAV/tree/zh-CN">中文</a> · <strong>English</strong></p>

# VoLN: Vision-Only Long-Horizon Navigation—Paradigm, Benchmark, and Method

<p align="center">
  <a href="https://admire-ljb.github.io/VoLN-UAV/">🌐 <strong>Project Page</strong></a>
  &nbsp;&nbsp;·&nbsp;&nbsp;
  <a href="https://arxiv.org/pdf/2607.21400">📄 <strong>Paper</strong></a>
  &nbsp;&nbsp;·&nbsp;&nbsp;
  <a href="https://huggingface.co/datasets/Louj/VoLN-UAV-dataset">🤗 <strong>Dataset</strong></a>
  &nbsp;&nbsp;·&nbsp;&nbsp;
  <a href="https://huggingface.co/datasets/Louj/VoLN-UAV-ENV">🧭 <strong>Simulator Environments</strong></a>
</p>

</div>

## Visual Demonstrations

<table>
  <tr>
    <th width="50%">Simulation — First-Person View</th>
    <th width="50%">Physical Flight — First-Person View</th>
  </tr>
  <tr>
    <td><img src="assets/readme/demos/simulation_demo.gif" alt="VoLN first-person simulation demonstration" width="100%"></td>
    <td><img src="assets/readme/demos/physical_flight_demo.gif" alt="VoLN first-person physical-flight demonstration" width="100%"></td>
  </tr>
</table>

## Reproduction Scope

This README is the reproducibility guide for the released VoLN training and evaluation code. For the method overview, headline results, dataset visualizations, qualitative cases, and simulation/real-world videos, visit the [project page](https://admire-ljb.github.io/VoLN-UAV/).

The repository covers:

- VoLN-MLLM adapter and planner training;
- VoLN-adapted Seq2Seq-VG, CMA-VG, and LAG-VG baselines;
- AirSim closed-loop benchmark evaluation;
- the No-Align, No-LoRA, and CLIP-Input ablations;
- metric reporting for NE, SR, OSR, nDTW, SPL, CT, and EER.

The [navigation dataset](https://huggingface.co/datasets/Louj/VoLN-UAV-dataset) and [simulator environments](https://huggingface.co/datasets/Louj/VoLN-UAV-ENV) are released separately and are not duplicated in this repository.

## Experimental Protocol

### Dataset splits

The manuscript uses **7,210 episodes**. Validation-Seen contains disjoint
trajectories from five environments represented in the training pool, while
Test-Unseen contains five held-out environments:

| Split | Episodes | Ratio | Evaluation name |
|---|---:|---:|---|
| Train | 5,047 | 70% | Train |
| Validation-Seen | 1,082 | 15% | Validation-Seen |
| Test-Unseen | 1,081 | 15% | Test-Unseen |

Difficulty is defined by reference path length:

- **Easy:** less than 300 m
- **Normal:** 300–450 m
- **Hard:** at least 450 m

### Model stages

VoLN-MLLM has two stages:

1. **Visual-semantic alignment.** A lightweight adapter maps frozen DINOv3 ViT-B/16 features into the frozen CLIP ViT-B/16 image-embedding space using cosine distillation.
2. **Trajectory planning.** A frozen Vicuna-7B-v1.5 backbone jointly encodes aligned observation history, three terminal goal views, proprioception, and top-$k$ category tokens retrieved from the fixed semantic bank. The released configuration uses $k=8$. Rank-16 LoRA modules adapt its attention and feed-forward projections; learned heads predict eight body-frame relative 3D waypoints and a stop signal.

### Evaluation rules

The trained baselines are visual-goal adaptations of instruction-following navigation models. All methods receive the same VoLN observations and share waypoint supervision, action interface, stopping rule, and evaluation protocol.

The policy receives onboard RGB and deployable body-frame proprioception; world-frame poses remain supervision/evaluation metadata. Training samples use the final three consecutive RGB frames as the visual goal and body-frame relative waypoint targets. The paper protocol uses at most 128 decisions per episode and a 4 m three-dimensional goal region. SR and SPL require the policy to issue an explicit stop inside that region; OSR records whether the executed trajectory enters it at any time. The stop threshold is calibrated on Validation-Seen and stored in `planner_best.pt`.

### Configuration index

| Experiment | Configuration or launcher |
|---|---|
| Adapter training | `configs/train_adapter_dataset_release.yaml` |
| Planner training | `configs/train_planner_dataset_release.yaml` |
| AirSim evaluation | `configs/eval_airsim_dataset_release.yaml` |
| Ablation experiments | `scripts/run_ablation_experiments.py` |
| Benchmark evaluation suite | `scripts/run_benchmark_evaluation.py` |
| Benchmark protocol audit | `scripts/validate_benchmark_protocol.py` |
| Experiment tables and figures | `scripts/compile_experiment_results.py` |
| Seq2Seq-VG / CMA-VG / LAG-VG | [Baseline documentation](docs/voln_adapted_baselines.md) |

## Installation

~~~bash
conda create -n voln-uav python=3.10 -y
conda activate voln-uav
pip install -e .
~~~

Install the CUDA-specific PyTorch build first if the default wheel does not match your system. The default package installation includes the training, AirSim, real-world, and plotting dependencies used by the released scripts.

### Ubuntu quick start

~~~bash
chmod +x scripts/*.sh
DEVICE=cuda ./scripts/train_planner.sh
DEVICE=cuda ./scripts/eval_offline.sh
~~~

For online evaluation, start a Linux Unreal/AirSim build and run the same
baseline wrapper:

~~~bash
./scripts/launch_simulator.sh --scene BrushifyUrban --executable /path/to/VolnEnv.sh
BASELINE=reference TRIALS=10 EVAL_MODE=normal \
  ./scripts/run_online_baseline.sh --work-dir runs/reference_test_10_normal
~~~

To use AirSim on another machine, additionally set `AIRSIM_IP` and
`AIRSIM_PORT`.

## Dataset Release Plan

- ✅ **Initial release:** 1,786 episodes across four environments: Brushify,
  BrushifyCountryRoads, BrushifyUrban, and BrushifyForestPack.
  - Train: 1,067 episodes (59.7%)
  - Validation-Seen: 319 episodes (17.9%)
  - Test-Unseen: 400 episodes (22.4%)
  - Difficulty: 917 Easy (51.3%), 627 Normal (35.1%), and 242 Hard (13.5%)
- ✅ **Simulator release:** AirSim environments are available separately from
  the navigation dataset.
- ⏳ **Full release:** expand the public benchmark to all 7,210 episodes across
  17 environments.

## Ground-Truth Trajectory Replay

Online evaluation requires the matching AirSim/Unreal scene to be running. After extracting the separately downloaded simulator package into `simulator_environments/`, launch the default Urban scene with:

~~~powershell
cd VoLN-UAV
.\scripts\launch_simulator.cmd --scene BrushifyUrban --env-root simulator_environments
~~~

The launcher resolves the scene executable from `configs/airsim_scene_mapping_dataset_release.json` and waits until the AirSim RPC port is ready. Keep the simulator open while evaluation is running. Use `--env-root` when the simulator package is stored elsewhere.

The following commands run **Ground-Truth Trajectory Replay** (a reference oracle) using the recorded ground-truth waypoint sequence. This diagnostic validates AirSim coordinate alignment, beacon/target placement, episode transitions, and metric logging. It is not a learned navigation baseline and should not be included as a competing method in benchmark tables. The wrapper reads `data/benchmark/episodes.jsonl` and selects the released `BrushifyUrban` training trajectories by default; set `EPISODES_FILE` or `SCENE` to override either choice. Select the execution mode with `EVAL_MODE`.

For online evaluation, active beacons are placed from the reference-route
motion using the original fixed-count selector. The release configuration uses
three active beacons, with deterministic candidate sampling, spacing checks,
and route-based fallback positions. The target is placed separately and is not
counted as an active beacon.

<details open>
<summary><strong>Ground-truth trajectory replay — normal speed</strong></summary>

~~~powershell
cd VoLN-UAV

$env:BASELINE="reference"
$env:TRIALS="10"
$env:EVAL_MODE="normal"
$env:EPISODES_FILE="episodes.jsonl"
$env:SCENE="BrushifyUrban"

.\scripts\run_online_baseline.cmd `
  --episode-index 0 `
  --episode-stride 1 `
  --reference-stride 1 `
  --work-dir runs/reference_test_10_normal
~~~

This mode teleports the vehicle to the exact episode start, clears residual motion, enters hover, and then follows the route with AirSim `move_to_position` commands. Route cues use direction-icon assets rather than text-label beacons. After the final waypoint, the vehicle brakes to zero velocity and remains hovering.

</details>

<details>
<summary><strong>Ground-truth trajectory replay — fast diagnostic</strong></summary>

~~~powershell
cd VoLN-UAV

$env:BASELINE="reference"
$env:TRIALS="10"
$env:EVAL_MODE="fast"
$env:EPISODES_FILE="episodes.jsonl"
$env:SCENE="BrushifyUrban"

.\scripts\run_online_baseline.cmd `
  --episode-index 0 `
  --episode-stride 1 `
  --reference-stride 1 `
  --work-dir runs/reference_test_10_fast
~~~

This mode uses `setVehiclePose` teleportation, pose-only reset, zero settling time, and a 10 m maximum teleport step.

</details>

Use `scripts\report_metrics.cmd` on Windows or
`./scripts/report_metrics.sh RUN_DIR` on Ubuntu to summarize a run directory.

## Training

Run the complete real-data pipeline:

~~~bash
python scripts/run_dataset_release_pipeline.py --device cuda
~~~

Run selected stages when resuming or debugging:

~~~bash
python scripts/run_dataset_release_pipeline.py --stages build train-adapter train-planner --device cuda
~~~

The VoLN-adapted baselines have separate training entry points and checkpoints. See [baseline documentation](docs/voln_adapted_baselines.md) for Seq2Seq-VG, CMA-VG, and LAG-VG.

Run the three manuscript ablations (`No-Align`, `No-LoRA`, and `CLIP-Input`) with their independent checkpoints:

~~~bash
python scripts/run_ablation_experiments.py --stages train airsim --device cuda
~~~

`No-Align` saves an untrained dimensional adapter without CLIP-teacher supervision, `No-LoRA` freezes Vicuna without inserting LoRA branches, and `CLIP-Input` feeds frozen CLIP ViT-B/16 image features directly to the planner.

## Evaluation

AirSim preflight and closed-loop evaluation:

~~~bash
python -m voln_uav.cli.eval_airsim --config configs/eval_airsim_dataset_release.yaml --preflight
python -m voln_uav.cli.eval_airsim --config configs/eval_airsim_dataset_release.yaml --device cuda
~~~

For a one-episode AirSim health check, run `scripts/run_airsim_eval_smoke.cmd` on Windows or `bash scripts/run_airsim_eval_smoke.sh` on Ubuntu.

The default evaluation controller is the checkpoint-free `random` baseline. To evaluate VoLN-MLLM, select the learned policy explicitly:

~~~bash
python -m voln_uav.cli.eval_airsim \
  --config configs/eval_airsim_dataset_release.yaml \
  --controller policy \
  --device cuda
~~~

Run all manuscript methods on Validation-Seen and Test-Unseen:

~~~bash
python scripts/run_benchmark_evaluation.py \
  --methods random seq2seq_vg cma lag voln_mllm \
  --splits validation_seen test_unseen \
  --device cuda
~~~

The manuscript launcher verifies the complete split before evaluation. For a
selected-scene diagnostic on a partial release, run:

~~~bash
python -m voln_uav.cli.eval_airsim \
  --config configs/eval_airsim_dataset_release.yaml \
  --split test_unseen \
  --scenes Campus Park Tunnel Ruins \
  --allow-partial-diagnostic \
  --device cuda
~~~

Each run writes `scene_coverage.json`. Add `--strict-scenes` to reject a missing
requested scene.

## Experimental Results and Consistency Checks

`configs/experiment_results.yaml` is the machine-readable source for the numbers
reported in the paper. Generated closed-loop logs are compared with this table
and summarized separately in `run_coverage.json`.

The committed result package is not limited to YAML. It includes normalized
JSON, long-form and wide-form CSV files, rendered Markdown tables, PNG/PDF
figures, run coverage, and per-metric comparison intermediates:

~~~text
results/experiments/
  experiment_results.json
  experiment_results.md
  experiment_results_long.csv
  run_coverage.json
  intermediate/
    README.md
    main_results_wide.csv
    ablation_results.csv
    run_comparison.csv
    result_manifest.json
  figures/
    test_unseen_sr.{png,pdf}
    test_unseen_ndtw.{png,pdf}
~~~

Export the experiment tables and plots:

~~~bash
python scripts/compile_experiment_results.py \
  --results configs/experiment_results.yaml \
  --output-dir results/experiments
~~~

Compare available closed-loop runs against the reported table:

~~~bash
python scripts/compile_experiment_results.py \
  --results configs/experiment_results.yaml \
  --output-dir results/experiments \
  --runs-root runs \
  --backend airsim
~~~

Missing run directories are listed as `skipped_missing` in
`run_coverage.json`. Use `--strict-runs` for release verification that must
include every method and split.

| Test-Unseen SR | Test-Unseen nDTW |
|---|---|
| ![Test-Unseen SR](https://raw.githubusercontent.com/Admire-ljb/VoLN-UAV/main/results/experiments/figures/test_unseen_sr.png) | ![Test-Unseen nDTW](https://raw.githubusercontent.com/Admire-ljb/VoLN-UAV/main/results/experiments/figures/test_unseen_ndtw.png) |

## Repository Structure

~~~text
src/voln_uav/
  benchmark/      Benchmark construction, visual goals, and beacon augmentation
  data/           Dataset loaders and release packaging
  models/         DINO–CLIP adapter, semantic bank, planners, and LoRA modules
  training/       Adapter/planner training and DAgger-style collection
  evaluation/     Offline and online metrics
  simulators/     Route replay and AirSim interfaces
  cli/            Command-line entry points
configs/          Dataset, training, and evaluation configurations
scripts/          Reproducible training and evaluation launchers
airsim_plugin/    Unreal/AirSim scene utilities
docs/             Project page, demonstrations, and baseline documentation
~~~

## Citation

If you find this work helpful, please consider citing our paper:

~~~bibtex
@article{lou2026voln,
  title  = {VoLN: Vision-Only Long-Horizon Navigation---Paradigm, Benchmark, and Method},
  author = {Lou, Jiabin and Wang, Haopeng and Wang, Yuanshuai and Liu, Xinyu and Lv, Xuxin and Guo, Yuxin and Huang, Lei and Shi, Rongye and Wu, Wenjun},
  journal = {arXiv preprint arXiv:2607.21400},
  year   = {2026},
  eprint = {2607.21400},
  archivePrefix = {arXiv},
  primaryClass = {cs.RO},
  url    = {https://arxiv.org/abs/2607.21400}
}
~~~

## Acknowledgement

We thank the authors of TravelUAV and AirVLN for releasing their codebases and providing useful engineering references for UAV navigation research.

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