DiffusionRL: Efficient Training of Diffusion Policies for Robotic Grasping Using RL-Adapted Large-Scale Datasets

Understand

Diffusion models have been successfully applied in areas such as image, video, and audio generation.

• Recent works show their promise for sequential decision-making and dexterous manipulation, leveraging their ability to model complex action distributions.
• However, challenges persist due to the data limitations and scenario-specific adaptation needs.
• In this paper, we address these challenges by proposing an optimized approach to training diffusion policies using large, pre-built datasets that are enhanced using Reinforcement Learning (RL).