| | --- |
| | license: mit |
| | tags: |
| | - diffusion-policy |
| | - imitation-learning |
| | - behavioral-cloning |
| | - robotics |
| | - push-task |
| | library_name: pytorch |
| | pipeline_tag: robotics |
| | --- |
| | |
| | # Push-F Diffusion Policy |
| |
|
| | A visuomotor diffusion policy trained to push an F-shaped block into a target orientation, adapted from the [Diffusion Policy](https://diffusion-policy.cs.columbia.edu/) 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} |
| | } |
| | ``` |
| | |
| | ## Links |
| | |
| | - [Full Report](https://github.com/bryandong24/reu_adaptation/blob/main/REPORT.md) |
| | - [Source Code](https://github.com/bryandong24/reu_adaptation) |
| | - [W&B Training Logs](https://wandb.ai/bryandong24-stanford-university/diffusion_policy_pushf) |
| | |
