# 3D Diffusion Policy

The work introduces Movement Trend Guidance: a compact latent of interaction evolution is learned from a short history of point clouds and robot states, supervised during training only by sparse future gripper states, and at inference retained alone as future-oriented conditioning, consistently improving 3D diffusion policies on RoboTwin2.0, LIBERO-40, DexArt and five real-robot tasks while adding only 3.52% more parameters than DP3.

The work introduces Movement Trend Guidance: a compact latent of interaction evolution is learned from a short history of point clouds and robot states, supervised during training only by sparse future gripper states, and at inference retained alone as future-oriented conditioning, consistently improving 3D diffusion policies on RoboTwin2.0, LIBERO-40, DexArt and five real-robot tasks while adding only 3.52% more parameters than DP3.