Grasping with anthropomorphic robotic hands involves much more hand-object
interactions compared to parallel-jaw grippers. Modeling hand-object
interactions is essential to the study of multi-finger hand dextrous
manipulation. This work presents DVGG, an efficient grasp generation network
that takes single-view observation as input and predicts high-quality grasp
configurations for unknown objects. In general, our generative model consists
of three components: 1) Point cloud completion for the target object based on
the partial observation; 2) Diverse sets of grasps generation given the
complete point cloud; 3) Iterative grasp pose refinement for physically
plausible grasp optimization. To train our model, we build a large-scale
grasping dataset that contains about 300 common object models with 1.5M
annotated grasps in simulation. Experiments in simulation show that our model
can predict robust grasp poses with a wide variety and high success rate. Real
robot platform experiments demonstrate that the model trained on our dataset
performs well in the real world. Remarkably, our method achieves a grasp
success rate of 70.7\% for novel objects in the real robot platform, which is a
significant improvement over the baseline methods.Comment: Accepted by Robotics and Automation Letters (RA-L, 2021