SwiftPolicy: Accelerated Visuomotor Policies via Score Distillation Sampling

📖 Overview

Diffusion Policy (DP) has established a new state-of-the-art in robotic manipulation by modeling policies as conditional denoising processes. However, the iterative nature of DP creates a significant latency bottleneck, requiring tens of network evaluations to generate a single action.

SwiftPolicy is a surprisingly simple distillation scheme to accelerate Diffusion Policy -> Achieving a 50x reduction in neural network evaluations (NFE)

📚 Resources

📄 Paper

The full project report is available as a PDF: MVA_Robots_25.pdf

🎥 Demo Video

Teacher vs Student policy running within the same time constraint

🚀 Quick Start

The entire training and evaluation pipeline is self-contained in a Google Colab notebook. You can run the code directly in your browser without any local setup.

Click the badge above to open the notebook.

🚧 Todo & Future Work

We are actively looking to improve SwiftPolicy. Current roadmap includes:

• [ ] Run on a real robot (if you have a robot we can borrow, please reach out!)
• [ ] Improving stability during training
• [ ] Scale to different datasets

✍️ Authors

• Duc-Hai Pham (duchai1092002 at gmail dot com) - ENS Paris-Saclay
• Ianis Hammani (ianis.hammani1712 at gmail dot com) - ENS Paris-Saclay

This project was developed as part of the MVA Master's program (Robotics course).

📝 Citation

If you find this code or work useful in your research, please cite the following:

@software{Pham_SwiftPolicy_Accelerated_Visuomotor_2025,
author = {Pham, Duc-Hai and Hammani, Ianis},
month = jan,
title = {{SwiftPolicy: Accelerated Visuomotor Policies via Score Distillation Sampling}},
url = {https://github.com/yourusername/swiftpolicy},
version = {1.0.0},
year = {2025}
}