grasp_diffusion

Introduction
The GRASP_DIFFUSION model provides trained models for SE(3) DiffusionFields, focusing on 6D grasping applications in robotics. It is designed to optimize grasp and motion through diffusion processes.

Architecture
The model architecture is based on SE(3)-DiffusionFields, which are used for learning smooth cost functions that aid in joint grasp and motion optimization in robotic systems. This approach facilitates effective 6D grasping capabilities.

Training
The model leverages diffusion processes to enhance the learning of cost functions. This training methodology is detailed in the associated research paper, which discusses the optimization of grasp and motion through diffusion.

Guide: Running Locally

1. Clone the repository:

   git clone https://huggingface.co/camusean/grasp_diffusion
   cd grasp_diffusion
   

2. Install dependencies as outlined in the project requirements.
3. Run the model using the provided scripts and configuration files.
4. It is recommended to use cloud GPUs for efficient processing, such as those available from AWS, Google Cloud, or Azure, especially for training and inference tasks.

License
The GRASP_DIFFUSION model is licensed under the Apache 2.0 License, permitting use, distribution, and modification under specified terms.