YanjieZe/3D-Diffusion-Policy

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1429 stars · 168 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

3D Diffusion Policy (DP3) is a visual imitation learning algorithm that combines compact 3D point cloud representations with diffusion policies for robot manipulation. It is the official PyTorch research codebase from an RSS 2024 paper, supporting training and evaluation on simulated and real-robot tasks.

Use cases

• train visuomotor policies from a handful of demonstrations
• learn robot manipulation skills from point cloud observations
• benchmark imitation learning baselines on simulated manipulation tasks
• deploy diffusion policies on real robot arms
• research generalizable robot policy learning with 3D representations

When to choose

• you need sample-efficient imitation learning for robot manipulation
• your robot observations include point clouds or depth cameras
• you want a strong, well-cited baseline for visuomotor policy research
• you have few human demonstrations (10-40) per task

When to avoid

• you need a production-ready robotics framework rather than research code
• your setup is RGB-only with no depth or point cloud data
• you lack a GPU or robotics simulation environment
• you want plug-and-play deployment without ML expertise

Facets

library · maturity active

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

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