| --- |
| license: apache-2.0 |
| datasets: |
| - lerobot/pusht |
| pipeline_tag: robotics |
| tags: |
| - robotics |
| --- |
| |
| # PushT Diffusion Policy - Robot Control Model |
|
|
| This model is an implementation of Diffusion Policy for the PushT environment, which simulates robotic pushing tasks. |
|
|
| ## Model |
|
|
| This model uses a conditional diffusion architecture to predict robotic actions based on visual observations. |
|
|
| ## Performance |
|
|
| The model achieves a success rate of 100.0% in the PushT environment with different initial configurations. |
|
|
| ## Demonstration Videos |
|
|
| The repository includes demonstration videos in the `videos/` folder. |
|
|
| ## Usage |
|
|
| ```python |
| from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy |
| |
| policy = DiffusionPolicy.from_pretrained("RafaelJaime/pusht-diffusion") |
| ``` |
|
|
| # Citation |
| ```bibtex |
| @article{chi2024diffusionpolicy, |
| author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song}, |
| title = {Diffusion Policy: Visuomotor Policy Learning via Action Diffusion}, |
| journal = {The International Journal of Robotics Research}, |
| year = {2024} |
| } |
| ``` |
| Published on 2025-04-28 |
