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README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- robotics
+- navigation
+- video-to-navigation
+- diffusion-transformer
+- optical-flow
+- GENESIS
+library_name: pytorch
+pipeline_tag: robotics
+---
+
+# FlowDiT V2 — Video-to-Navigation (GENESIS)
+
+Part of the **GENESIS** research framework: video-conditioned robot learning.
+
+**Paper**: [Action Agent: Agentic Video Generation Meets Flow-Constrained Diffusion](https://arxiv.org/abs/2605.01477) (IROS 2026)
+
+**Code**: [github.com/jeffrinsam/GENESIS](https://github.com/jeffrinsam/GENESIS) → `part2_navigation/`
+
+## Model Description
+
+FlowDiT V2 is a flow-constrained Diffusion Transformer (DiT) that translates a reference goal video and a current observation image into continuous navigation commands `[vx, vy, yaw_rate]`.
+
+**Architecture:**
+- **Visual encoder**: DINOv2-ViT-B/14 (frozen) — extracts spatial features from current observation
+- **Flow encoder**: RAFT optical flow → temporal flow tokens from goal video
+- **DiT backbone**: Diffusion Transformer with cross-attention between flow tokens and obs features
+- **Output**: 3-DOF velocity command at 2 Hz control frequency
+
+**Training data**: ~50k episodes across wheeled and legged embodiments (Unitree B1, G1, custom wheeled platforms) in Isaac Sim.
+
+## Performance
+
+Evaluated on 41 tasks across 3 robot embodiments in Isaac Sim:
+
+| Metric | Value |
+|--------|-------|
+| Success Rate (SR @ 3.0 m) | 100% |
+| SPL | 0.91 |
+| Avg Trajectory Error (ATE) | 0.42 m |
+| Direction Accuracy (cosine > 0.75) | 96.3% |
+
+## Usage
+
+```bash
+# Install dependencies
+conda activate genesis-navigation
+cd GENESIS
+
+# Single inference
+python part2_navigation/flow_constrained_v2/inference.py \
+  --checkpoint path/to/best.pth \
+  --goal_video reference.mp4 \
+  --current_obs frame.jpg \
+  --output actions.npy
+```
+
+Download via the GENESIS checkpoint script:
+```bash
+bash scripts/download_checkpoints.sh
+```
+
+## Checkpoint Details
+
+| File | Size | Format |
+|------|------|--------|
+| `best.pth` | 1.2 GB | PyTorch state dict + config |
+
+The `.pth` file contains:
+```python
+{
+  "model_state_dict": ...,
+  "config": {"use_raft": False, "hidden_dim": 512, ...},
+  "epoch": ...,
+  "val_loss": ...
+}
+```
+
+## Citation
+
+```bibtex
+@inproceedings{sam2026actionagent,
+  title     = {Action Agent: Agentic Video Generation Meets Flow-Constrained Diffusion},
+  author    = {Sam, Jeffrin and Khang, Nguyen and Mahmoud, Yara and
+               Altamirano Cabrera, Miguel and Tsetserukou, Dzmitry},
+  booktitle = {2026 IEEE/RSJ International Conference on Intelligent Robots
+               and Systems (IROS)},
+  year      = {2026},
+  note      = {arXiv:2605.01477}
+}
+```
+
+## License
+
+Apache 2.0. See [LICENSE](https://github.com/jeffrinsam/GENESIS/blob/main/LICENSE).