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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
+- humanoid
+- GENESIS
+- unitree-g1
+library_name: pytorch
+pipeline_tag: robotics
+---
+
+# FlowDiT V3 Humanoid — 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/flow_constrained_v3_humanoid/`
+
+## Model Description
+
+FlowDiT V3 Humanoid is an inference-optimized Diffusion Transformer specialized for **bipedal humanoid navigation** (Unitree G1). It extends FlowDiT V2 with humanoid-specific motion constraints and whole-body balance priors.
+
+**Architecture:**
+- **Visual encoder**: DINOv2-ViT-B/14 (frozen)
+- **Flow encoder**: RAFT optical flow with humanoid-specific temporal attention
+- **DiT backbone**: Enlarged Diffusion Transformer with balance-constraint cross-attention
+- **Output**: 3-DOF velocity command `[vx, vy, yaw_rate]` + gait phase signal
+
+**Target robot**: Unitree G1 humanoid (inference only — see code for the training pipeline).
+
+**Runtime**: PyTorch 2.9.1+cu128, requires ~4 GB VRAM for inference.
+
+## Performance
+
+Evaluated on Unitree G1 in Isaac Sim navigation tasks:
+
+| Metric | Value |
+|--------|-------|
+| Success Rate (SR @ 3.0 m) | 100% |
+| SR @ 1.0 m (post-processed) | ~39% |
+| Avg Trajectory Error (ATE) | 0.38 m |
+
+## Usage
+
+```bash
+# Activate the V3 inference venv (torch 2.9.1+cu128)
+cd GENESIS/part2_navigation/flow_constrained_v3_humanoid
+source .venv/bin/activate
+
+python infer_humanoid.py \
+  --checkpoint flowdit_v3_humanoid_best.pt \
+  --goal_video goal.mp4 \
+  --current_obs obs.jpg
+```
+
+Download via the GENESIS checkpoint script:
+```bash
+bash scripts/download_checkpoints.sh
+```
+
+## Checkpoint Details
+
+| File | Size | Format |
+|------|------|--------|
+| `flowdit_v3_humanoid_best.pt` | 982 MB | PyTorch state dict + config |
+
+## 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).