3D Diffusion Policy
Authors: name: Yanjie Ze, name: Gu Zhang, name: Kangning Zhang, name: Chenyuan Hu, name: Muhan Wang, name: Huazhe Xu

Imitation learning provides an efficient way to teach robots dexterous
skills; however, learning complex skills robustly and generalizablely usually
consumes large amounts of human demonstrations. To tackle this challenging
problem, we present 3D Diffusion Policy (DP3), a novel visual imitation
learning approach that incorporates the power of 3D visual representations into
diffusion policies, a class of conditional action generative models. The core
design of DP3 is the utilization of a compact 3D visual representation,
extracted from sparse point clouds with an efficient point encoder. In our
experiments involving 72 simulation tasks, DP3 successfully handles most tasks
with just 10 demonstrations and surpasses baselines with a 55.3% relative
improvement. In 4 real robot tasks, DP3 demonstrates precise control with a
high success rate of 85%, given only 40 demonstrations of each task, and shows
excellent generalization abilities in diverse aspects, including space,
viewpoint, appearance, and instance. Interestingly, in real robot experiments,
DP3 rarely violates safety requirements, in contrast to baseline methods which
frequently do, necessitating human intervention. Our extensive evaluation
highlights the critical importance of 3D representations in real-world robot
learning. Videos, code, and data are available on
https://3d-diffusion-policy.github.io .