<a href="https://demo-generation.github.io/">𝑫𝒆𝒎𝒐𝑮𝒆𝒏: Synthetic Demonstration Generation for Data-Efficient Visuomotor Policy Learning</a>

<a href="https://demo-generation.github.io/"><strong>Project Page</strong></a> | <a href="https://arxiv.org/abs/2502.16932"><strong>arXiv</strong></a> | <a href="https://x.com/ZhengrongX/status/1899134914416800123"><strong>Twitter</strong></a>

Robotics: Science and Systems (RSS) 2025

🎯 Overview

<div align="center"> <img src="pics/teaser.png" alt="teaser" width="100%"> </div>

𝑫𝒆𝒎𝒐𝑮𝒆𝒏 is a synthetic data generation approach designed for robotic manipulation. Given only one human demonstration collcted in the real world, 𝑫𝒆𝒎𝒐𝑮𝒆𝒏 could produce hundreds of spatially-augmented synthetic demonstrations in few seconds. These demos are proved to be effective for training visuomotor policies (e.g., DP3) with strong O.O.D. generalization capabilities.

<br> <div align="center"> <img src="pics/method.png" alt="method" width="100%"> </div> For action generation, 𝑫𝒆𝒎𝒐𝑮𝒆𝒏 adopts the idea of Task and Motion Planning (TAMP) and adapts the source actions according to novel object configurations. For visual observation generation, 𝑫𝒆𝒎𝒐𝑮𝒆𝒏 leverages 3D point clouds as the modality and rearranges the subjects in the scene via 3D editing.

🐣 Update

• 2025/04/02, Officially released 𝑫𝒆𝒎𝒐𝑮𝒆𝒏.

🚀 Quick Try in 5 Minutes

1. Minimal Installation

1.0. Create conda Env

conda remove -n demogen --all
conda create -n demogen python=3.8
conda activate demogen

1.1. Install pip Packages

pip3 install imageio imageio-ffmpeg termcolor hydra-core==1.2.0 zarr==2.12.0 matplotlib setuptools==59.5.0 pynput h5py scikit-video tqdm

1.2. Install diffusion_policies

We only need the dataset loader in the diffusion_policies package.

cd diffusion_policies
pip install -e .
cd ..

1.3. Install 𝑫𝒆𝒎𝒐𝑮𝒆𝒏

cd demo_generation
pip install -e .
cd ..

2. Generate Synthetic Demos Using 𝑫𝒆𝒎𝒐𝑮𝒆𝒏

2.1. The 𝑫𝒆𝒎𝒐𝑮𝒆𝒏 implementation

The 𝑫𝒆𝒎𝒐𝑮𝒆𝒏 procedure is implemented in demo_generation/demo_generation/demogen.py. To run the code, you need to specify a .yaml config file under the demo_generation/demo_generation/config folder, where we provide some examples for your reference. The outer entrance that combines the main code and configs is demo_generation/gen_demo.py.

2.2. Inputs & Outputs

We prepare some .zarr datasets consisting of 1~3 source demos under the folder data/datasets/source. By running the gen_demo.py script with proper config file, you can generate datsets of synthetic demos, which will be placed under the data/datasets/generated folder. To get a sense of what has been generated, you can check the rendered videos under the data/videos folder, when the generation:render_video flag is set to True in the config file.

Note: While the demo generation process is very fast, it takes ~10s to render the video for a single generated trajectory. So we recommend rendering videos only for debugging purpose.

2.3. Demo Generation!

We provide some example generation commands in the demo_generation/run_gen_demo.sh script, including four tasks: Flower-Vase, Mug-Rack, Spatula-Egg, and Sauce-Spreading. You can try running it and compare the results of synthetic and source demos in the data/datasets/generated and data/videos folders, where the filename of the videos indicate how the objects are transformed.

cd demo_generation
bash run_gen_demo.sh

🛠️ Run On Your Own Tasks

As long as your task requires to collect a handful of demonstrations to overcome the spatial generalization problem, 𝑫𝒆𝒎𝒐𝑮𝒆𝒏 could be your remedy for saving the repetitive human labor. As is proved by the experiments we have conducted in our paper, 𝑫𝒆𝒎𝒐𝑮𝒆𝒏 is generally effective for various types of tasks, even those involving contact-rich motion skills. To help you apply 𝑫𝒆𝒎𝒐𝑮𝒆𝒏 to your own task, we prepare a detailed guide under the docs folder. Check it out if you are interested!

🏷️ License

This repository is released under the MIT license. See LICENSE for additional details.

👍 Acknowledgement

Our code is generally built upon: 3D Diffusion Policy, Diffusion Policy, UMI, MimicGen. We thank all these authors for their nicely open sourced code and their great contributions to the community.

Contact Zhengrong Xue if you have any questions or suggestions.

📝 Citation

If you find our work useful, please consider citing:

@article{xue2025demogen,
  title={DemoGen: Synthetic Demonstration Generation for Data-Efficient Visuomotor Policy Learning},
  author={Xue, Zhengrong and Deng, Shuying and Chen, Zhenyang and Wang, Yixuan and Yuan, Zhecheng and Xu, Huazhe},
  journal={arXiv preprint arXiv:2502.16932},
  year={2025}
}