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Diffusion Policy
A visuomotor policy approach using diffusion to generate robot actions.
Robot policies
Models / Research
deep rl grasping · BarisYazici
Deep RL Grasping provides training and evaluation code for depth/RGB-D robotic grasping policies.
Overview
Deep RL Grasping provides training and evaluation code for depth/RGB-D robotic grasping policies.
Publisher summary: “Train deep reinforcement learning model for robotics grasping. Choose from different perception layers raw Depth, RGBD and autoencoder. Test the learned models in different…”
Use its repository to inspect the implementation, documentation and supported workflows. This directory record identifies the maintainer, code license and related resources; it does not claim that the project has been installed or tested on your robot.
Project information
- Maintainer
- BarisYazici
- Primary language
- Python
- Repository code license
- MIT
- Model-weight license
- Not recorded
- Dataset license
- Not recorded
- Hardware design license
- Not recorded
- Maintenance
- Not archived in the last recorded metadata
- Last observed repository push
- 2022-07-10
Code licenses do not automatically cover model weights, datasets or hardware designs. Unrecorded license details require confirmation with the publisher.
Documentation sections
- Deep Reinforcement Learning on Robotics Grasping
- Prerequisites (CPU)
- Prerequisites (GPU)
- Installation
- Run Models
- Train models
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