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bryandong/
pushf-diffusion-policy

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Push-F Diffusion Policy

A visuomotor diffusion policy trained to push an F-shaped block into a target orientation, adapted from the Diffusion Policy codebase (Chi et al., 2023).

Model Description

Architecture: Diffusion UNet with ResNet18 image encoder
Parameters: 278M
Observations: 96x96 RGB image + 2D agent position
Actions: 2D target position for the agent
Training data: 101 human demonstrations (~29,800 timesteps)
Training: 250 epochs on NVIDIA H100, ~3.5 hours
Framework: PyTorch 2.0.1

Performance

Evaluated on 50 held-out environment seeds:

Time Limit	Mean Score	Perfect Seeds (1.0)
30s	0.837	19/50
45s	0.945	38/50
60s	0.961	45/50
90s	1.000	50/50

Usage

bash

git clone https://github.com/bryandong24/reu_adaptation.git
cd reu_adaptation

# Set up environment
mamba env create -f conda_environment.yaml
conda activate robodiff
pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
pip install -e .

# Download checkpoint and evaluate
python eval.py --checkpoint epoch=0250-test_mean_score=0.880.ckpt -o eval_output

Training Details

Loss: MSE denoising loss (DDPM)
Optimizer: AdamW (lr=1e-4, weight_decay=1e-6)
LR Schedule: Cosine with 500-step warmup
Batch size: 64
Horizon: 16 steps (n_obs=2, n_action=8)
Diffusion steps: 100 (training), 100 (inference)
EMA: Enabled

Citation

Based on:

bibtex

@inproceedings{chi2023diffusionpolicy,
    title={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
    author={Chi, Cheng and Feng, Siyuan and Du, Yilun and Xu, Zhenjia and Cousineau, Eric and Burchfiel, Benjamin and Song, Shuran},
    booktitle={Proceedings of Robotics: Science and Systems (RSS)},
    year={2023}
}

bryandong/pushf-diffusion-policy