real-stanford/diffusion_policy

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4491 stars · 830 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Official PyTorch implementation of Diffusion Policy, a visuomotor robot policy learning method that represents robot behavior as a conditional denoising diffusion process. It includes training/evaluation code, experiment configs, checkpoints, and Colab notebooks for state-based and vision-based manipulation tasks.

Use cases

• train diffusion-based robot manipulation policies from demonstrations
• reproduce visuomotor policy learning experiments from the RSS 2023 paper
• benchmark imitation learning methods on robot manipulation tasks
• run pretrained policies in simulation environments
• experiment with diffusion models for multimodal action distribution learning

When to choose

• you need state-of-the-art visuomotor policy learning for robot manipulation
• you want to reproduce or extend published diffusion policy experiments
• your task has multimodal action distributions that Gaussian policies handle poorly

When to avoid

• you need a lightweight production robot control stack rather than research code
• you lack GPU resources for diffusion training and inference
• your domain is unrelated to robot learning or manipulation

Facets

library · maturity stable

machine-learning deep-learning robotics simulation robotics machine-learning deep-learning artificial-intelligence python diffusion-models imitation-learning visuomotor-policy robot-manipulation research-code linux gpu

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