Create README.md

README.md

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+---
+license: mit
+pretty_name: HoRD Models
+language:
+- en
+tags:
+- robotics
+- humanoid-robot
+- reinforcement-learning
+- motion-imitation
+- policy-learning
+---
+
+# HoRD Models: Checkpoints for Robust Humanoid Motion Control
+
+This model card describes the released checkpoints for **HoRD** (History-Conditioned Reinforcement Learning and Online Distillation), a two-stage framework for robust humanoid control under domain shift.
+
+- **Paper**: [HoRD: Robust Humanoid Control via History-Conditioned Reinforcement Learning and Online Distillation](https://arxiv.org/abs/2602.04412)
+- **Project Page**: [https://tonywang-0517.github.io/hord/](https://tonywang-0517.github.io/hord/)
+- **Code Repository**: [https://github.com/tonywang-0517/hord](https://github.com/tonywang-0517/hord)
+- **Dataset Repository**: [https://huggingface.co/datasets/tony0517/HoRD](https://huggingface.co/datasets/tony0517/HoRD)
+- **Model Repository**: [https://huggingface.co/tony0517/HoRD](https://huggingface.co/tony0517/HoRD)
+
+## Model Overview
+
+HoRD checkpoints are trained for robust humanoid motion imitation and control with online adaptation capability.
+
+- **Stage 1 (Teacher RL)**: an expert policy trained with privileged observations and domain randomization.
+- **Stage 2 (Online Distillation)**: a deployable student policy distilled from the teacher under partial observability and sparse commands.
+
+These checkpoints are intended for evaluation and downstream experiments in IsaacLab and Genesis settings.
+
+## Model Contents
+
+Typical release artifact:
+
+- `your_checkpoint.ckpt`: pretrained HoRD checkpoint for evaluation or fine-tuning.
+
+## Quick Start
+
+Install Hugging Face CLI:
+
+```bash
+pip install -U "huggingface_hub[cli]"
+```
+
+Download a checkpoint from the model repository:
+
+```bash
+mkdir -p results
+huggingface-cli download tony0517/HoRD your_checkpoint.ckpt --local-dir results --local-dir-use-symlinks False
+```
+
+Use in HoRD evaluation:
+
+```bash
++checkpoint=results/your_checkpoint.ckpt
+```
+
+## Example Evaluation Command
+
+```bash
+python hord/eval_agent.py +exp=full_body_tracker/transformer +robot=g1 +simulator=isaaclab +motion_file=data/train_g1_all.pt +experiment_name=full_body_tracker ++headless=False +checkpoint=results/your_checkpoint.ckpt ++num_envs=1
+```
+
+## License
+
+This model card is released under the **MIT License**.
+
+## Citation
+
+If you find these checkpoints useful, please cite:
+
+```bibtex
+@article{wang2026hord,
+  title={HoRD: Robust Humanoid Control via History-Conditioned Reinforcement Learning and Online Distillation},
+  author={Wang, Puyue and Hu, Jiawei and Gao, Yan and Wang, Junyan and Zhang, Yu and Dobbie, Gillian and Gu, Tao and Johal, Wafa and Dang, Ting and Jia, Hong},
+  journal={arXiv preprint arXiv:2602.04412},
+  year={2026}
+}
+```