---
license: mit
tags:
  - autonomous-driving
  - motion-planning
  - flow-matching
  - generative-model
  - navsim
library_name: pytorch
pipeline_tag: other
---

# FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning

FlowR2A is a generative multimodal driving planner that learns the **reward-conditioned action distribution** p(a|r) with **flow matching**. Instead of treating simulation-based rewards as *discriminative targets* (as in scoring-based planners), FlowR2A reframes them as *generative conditions*, unifying the dense supervision of scoring-based methods with the dynamic proposal generation of anchor-based methods in a single model. This forces the planner to internalize how an action relates to its outcomes in safety, progress, comfort, and rule compliance.

- 📄 **Paper:** https://arxiv.org/abs/2606.24231
- 🌐 **Project page:** https://lixirui142.github.io/flowr2a-project-page/
- 💻 **Code:** https://github.com/lixirui142/FlowR2A

## Model Description

FlowR2A consists of four components:

1. **Perception Encoder** — a Transfuser backbone (multi-view camera + BEV LiDAR) producing scene and agent tokens.
2. **Reward Encoder** — embeds simulation reward signals (safety, progress, comfort, rule compliance) into a condition vector injected via adaptive layer norm; supports classifier-free guidance through reward dropout.
3. **Flow-based Action Decoder** — a transformer with self-attention over trajectory points and cross-attention to scene tokens, conditioned on reward + time embeddings via AdaLN, trained with a velocity-matching loss over dense action–reward pairs.
4. **Mode Selector** — a lightweight transformer that scores generated proposals, trained with online simulation labeling.

## Checkpoint

| File | Description |
|------|-------------|
| `flowr2a_s2.ckpt` | Stage-2 checkpoint, including all components. |

## Results

State-of-the-art closed-loop performance on the NAVSIM `navtest` benchmarks (lightweight backbone).

**NAVSIM v1**

| Setting | NC | DAC | TTC | Comf. | EP | **PDMS** |
|---|---|---|---|---|---|---|
| Single proposal | 98.6 | 97.3 | 95.3 | 100 | 84.9 | **90.0** |
| 60 proposals | 98.8 | 98.0 | 96.0 | 100 | 90.1 | **92.8** |

**NAVSIM v2**

| NC | DAC | DDC | TLC | EP | TTC | LK | HC | EC | **EPDMS** |
|---|---|---|---|---|---|---|---|---|---|
| 98.9 | 98.1 | 99.1 | 99.7 | 91.5 | 98.5 | 95.0 | 98.3 | 65.2 | **88.9** |

## Usage

See the [GitHub repository](https://github.com/lixirui142/FlowR2A) for setup, the NAVSIM data pipeline, and inference instructions. Download the checkpoint with:

```python
from huggingface_hub import hf_hub_download

ckpt = hf_hub_download(repo_id="lixirui142/FlowR2A", filename="flowr2a_s2.ckpt")
```

## Citation

```bibtex
@article{flowr2a2026,
  title   = {FlowR2A: Learning Reward-to-Action Distribution for Multimodal Driving Planning},
  author  = {Li, Xirui and Liu, Zhe and Ye, Xiaoqing and Han, Wenhua and Pan, Yifeng and Han, Junyu and Zhao, Hengshuang},
  journal = {arXiv preprint},
  year    = {2026}
}
```

## License

Released under the MIT License.