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README.md

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+# Sim2Sim2Sim Checkpoints
+
+Official pretrained checkpoints for [Dynamics Distillation for Efficient and Transferable Control Learning](https://arxiv.org/abs/2605.01516).
+
+## Directory Structure
+
+```
+dynamics_model/              # Stage 1: Learned dynamics models
+├── beamng_bicycle_*/        # Bicycle model variants
+├── beamng_ddm_*/            # Deep Dynamics Model (DDM) variants
+├── beamng_trans_*/          # Transformer-based models
+├── beamng_dytr_*/           # DYTR (Dynamics Transfer) models
+└── beamng_manual_PID_*/     # Manual PID control baselines
+
+control_policies/            # Stage 2 & 3: Trained RL policies
+├── PPO____*_bicycle/        # Policies trained on bicycle dynamics
+├── PPO____*_ddm/            # Policies trained on DDM dynamics
+├── PPO____*_trans/          # Policies trained on Transformer dynamics
+├── PPO____*_dytr_ddm/       # Policies trained on DYTR dynamics
+└── PPO____*_oracle/         # Oracle policies (full-state observation)
+```
+
+## Usage
+
+### Download Specific Model
+
+```python
+from huggingface_hub import hf_hub_download
+
+# Download a dynamics model
+dynamics_model = hf_hub_download(
+    repo_id="alfredgu001324/Sim2Sim2Sim",
+    filename="dynamics_model/beamng_trans_10/best_model.pt"
+)
+
+# Download a trained policy
+policy = hf_hub_download(
+    repo_id="alfredgu001324/Sim2Sim2Sim",
+    filename="control_policies/PPO____R_80000__11_25_10_41_44_840_trans/model_PPO____R_80000__11_25_10_41_44_840_001280.pt"
+)
+```
+
+### Batch Download
+
+```python
+from huggingface_hub import snapshot_download
+
+# Download all checkpoints
+local_dir = snapshot_download(
+    repo_id="alfredgu001324/Sim2Sim2Sim",
+    cache_dir="./ckpts",
+    repo_type="model"
+)
+```
+
+## Model Details
+
+### Dynamics Models (Stage 1)
+
+- **Bicycle**: Simple analytical model serving as baseline
+- **DDM**: Deep Dynamics Model - neural network trained on BeamNG data
+- **Transformer**: Sequence-aware dynamics model using transformer architecture
+- **DYTR**: Dynamics Transfer model for cross-domain generalization
+
+### Control Policies (Stage 2 & 3)
+
+All policies trained using PPO with 80,000 environment steps:
+- **trans**: Trained on Transformer dynamics model
+- **ddm**: Trained on DDM dynamics model  
+- **dytr_ddm**: Trained on DYTR-wrapped DDM dynamics model
+- **bicycle**: Trained on bicycle model
+- **oracle**: Full-state observation policies
+- **FF**: Feed-forward policies
+- **trans_cond**: Transformer with condition encoding for surface changes
+
+## Citation
+
+If you use these checkpoints, please cite the paper:
+
+```bibtex
+@article{GuChittaEtAl2026,
+  author    = {Gu, Xunjiang and Chitta, Kashyap and Golchoubian, Mahsa and Suplin, Vladimir and Gilitschenski, Igor},
+  title     = {Dynamics Distillation for Efficient and Transferable Control Learning},
+  journal   = {arXiv preprint arXiv:2605.01516},
+  year      = {2026}
+}
+```
+
+## License
+
+Apache 2.0