File size: 2,960 Bytes
7bdebfa 1225971 7bdebfa 1225971 7bdebfa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | # 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 (Residual Learning) 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**: Residual Learning towards High-fidelity Vehicle Dynamics Modeling with Transformer
### 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
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