README.md

Sim2Sim2Sim Checkpoints

Official pretrained checkpoints for Dynamics Distillation for Efficient and Transferable Control Learning.

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

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

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:

@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