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license: apache-2.0
library_name: pytorch
tags:
- robotics
- uav
- drone
- flow-matching
- autonomous-navigation
- flightmare
---
# FlowPilot Checkpoints
Model checkpoints for **FlowPilot**, a Flow Matching based world-action model for real-time onboard UAV navigation.
The model uses a dual-stream MoT to jointly model video latents and action tokens. Actions are parameterized by a 7th-order Bernstein polynomial with 8 control points (3 fixed by the initial state and 5 predicted by the network), producing 48 waypoints at 30 Hz.
- Code: [flyingbitac/FlowPilot](https://github.com/flyingbitac/FlowPilot)
## Files
| File | Size | Description |
| --- | --- | --- |
| `ckpt/phase2/ckpt_final.pt` | 651 MB | Phase-2 checkpoint. The inference state dict is stored under the `model` key; `step`, `optimizer`, and `lr_scheduler` are also included. |
| `ckpt/action_stats.pt` | 3.1 KB | Action and state normalization statistics. |
| `ckpt/coeff_stats.pt` | 2.7 KB | Bernstein coefficient normalization statistics. |
The Wan2.2 VAE weights are not stored in this repository; download the official file from [Wan-AI/Wan2.2-TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B) as shown below.
## Usage
Clone the code repository first, then download the checkpoints into its root:
```bash
git clone https://github.com/flyingbitac/FlowPilot.git
cd FlowPilot
hf download YuDinging/FlowPilot --include "ckpt/*" --local-dir .
hf download Wan-AI/Wan2.2-TI2V-5B Wan2.2_VAE.pth --local-dir ckpt
```
If `hf` is not available, install it with `pip install -U huggingface_hub`.
The `--include "ckpt/*"` filter matters: without it, `hf download` would also write this model card as `README.md` and overwrite the README of the code repository.
The files land under `ckpt/`, the default location expected by `configs/runtime/rviz.py`:
```
ckpt/
├── phase2/ckpt_final.pt
├── Wan2.2_VAE.pth
├── action_stats.pt
└── coeff_stats.pt
```
`hf download` also creates a `.cache/huggingface/` directory in the current directory. It only holds download metadata and can be removed afterwards. If you prefer to keep the weights outside the repository, download to another directory with `--local-dir` and symlink its `ckpt/` into the repository instead.
Then start the simulation from the code repository:
```bash
python docker/docker.py build
python docker/docker.py start
```
## Intended Use
These weights are released for research on learning-based UAV navigation, mainly for simulation and onboard inference experiments. They are not certified for safety-critical flight.
## License
The checkpoints are released under the Apache License 2.0.
`ckpt/Wan2.2_VAE.pth` is downloaded from the official [Wan2.2 model repository](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B) (Alibaba Wan Team) and is also licensed under Apache-2.0.
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