# Robot Motion Planning

Diffusion Policy is a diffusion model-based policy learning framework suitable for robotic control tasks.

A robot control model based on diffusion policy, released via PyTorchModelHubMixin integration

DRP is a model focused on robot motion planning, trained for 7k epochs (approximately 7M steps) under a 5M checkpoint

A machine learning-based motion planning system for robotic manipulation tasks, combining neural networks with optimization techniques to generate collision-free motion trajectories

A robot control model trained using Diffusion Policy, specifically designed for PushT tasks, utilizing keypoint observation data for training

Diffusion Policy is a visual-motor policy learning model based on action diffusion, specifically designed for the PushT environment.