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[RSS 2023] Diffusion Policy Visuomotor Policy Learning via Action Diffusion
Explore Diffusion Policy, a visuomotor policy learning method leveraging action diffusion models. Learn how this project addresses challenges in learning complex robotic manipulation skills directly from visual inputs.
Python
Added on 2025年6月7日2,623
Stars473
ForksPython
LanguageProject Introduction
Summary
This project introduces Diffusion Policy, a novel approach for learning visuomotor policies based on action diffusion models. By framing policy learning as an action generation process via diffusion, it offers a powerful method for tackling complex robotic manipulation tasks from visual observations.
Problem Solved
Traditional visuomotor policy learning methods often struggle with capturing complex, multi-modal action distributions and can be sensitive to variations in visual input, limiting their effectiveness in real-world robotic tasks. Diffusion Policy aims to provide a more powerful and flexible approach.
Core Features
Action Diffusion Modeling
Utilizes a conditional diffusion model to learn a distribution over actions given visual observations, enabling the generation of diverse and complex behaviors.
End-to-End Visuomotor Learning
Enables end-to-end policy learning directly from raw pixel data, simplifying the pipeline from perception to action.
Robustness & Multi-modal Actions
Demonstrates robustness and ability to handle multi-modal action distributions inherent in many dexterous manipulation tasks.
Tech Stack
Python
PyTorch
Gym/Isaac Gym (for simulation)
Various Robotics Libraries
使用场景
Diffusion Policy is applicable to various robotic tasks that require learning complex motor skills from visual input, particularly those involving dexterous manipulation and handling uncertainty.
场景一：灵巧机械臂操作
Details
Learning complex grasping and manipulation policies for diverse objects from camera images, potentially handling cluttered scenes or variations in object pose.
User Value
Enables robots to perform more complex and human-like manipulation tasks reliably in unstructured environments.
场景二：基于视觉的机器人导航
Details
Developing navigation policies for mobile robots where actions need to be conditioned on camera input to avoid obstacles or reach specific targets.
User Value
Provides a powerful method for learning robust navigation strategies directly from perceptual data.
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