Publication
Grasp Diffusion Network: Learning Grasp Generators from Partial Point Clouds with Diffusion Models in SO(3)xR3
Joao Carvalho; An T. Le; Philipp Jahr; Qiao Sun; Julen Urain; Dorothea Koert; Jan Peters
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2412.08398, Pages 1-8, arXiv, 2024.
Abstract
Grasping objects successfully from a single-view
camera is crucial in many robot manipulation tasks. An
approach to solve this problem is to leverage simulation to
create large datasets of pairs of objects and grasp poses,
and then learn a conditional generative model that can be
prompted quickly during deployment. However, the grasp pose
data is highly multimodal since there are several ways to
grasp an object. Hence, in this work, we learn a grasp
generative model with diffusion models to sample candidate
grasp poses given a partial point cloud of an object. A novel
aspect of our method is to consider diffusion in the manifold
space of rotations and to propose a collision-avoidance cost
guidance to improve the grasp success rate during inference.
To accelerate grasp sampling we use recent techniques from
the diffusion literature to achieve faster inference times. We
show in simulation and real-world experiments that our ap-
proach can grasp several objects from raw depth images with
90% success rate and benchmark it against several baselines.
https://sites.google.com/view/graspdiffusionnetwork
Index Terms–Deep Learning, Learning to Grasp, Diffusion
Models.