Abstract
Grasp planning is an important task for robotic manipulation. Though it is a richly studied area, a standalone, fast, and differentiable grasp planner that can work with robot grippers of different DOFs has not been reported. In this work, we present DiPGrasp, a grasp planner to satisfy all these goals. DiPGrasp takes a geometric surface matching grasp quality metric. It adopts a gradient-based optimization scheme on the metric which also considers parallel sampling and collision handling. This not only drastically accelerates the grasp search process over the object surface but also makes it differentiable. We apply DiPGrasp to three applications, namely grasp dataset construction, mask-conditioned planning, and pose refinement. For dataset generation, as a standalone planner, DiPGrasp has clear advantages over speed and quality in comparison with several classic planners. For mask-conditioned planning, it can turn a 3D perception model into a 3D grasp detection model instantly. As a pose refiner, it can optimize the coarse grasp prediction from the neural network, as well as the neural network parameters. Finally, we conduct real-world experiments with the Barrett hand and Schunk SVH 5-finger hand.
Method
DiPGrasp takes a point cloud with normal as input. It first samples locations on the point cloud (red dot) and initializes the pose accordingly. Then it operates the differentiable optimization process to generate the grasps.
Applications
Grasp Dataset Generation
Extremely fast planning: For a Schunk SVH hand, it takes 2.5s to search over 80 locations (depends on GPU memory), results in 21 valid grasp. That is 118ms for a valid grasp in average. For a Barrett hand, it takes 30ms for a valid grasp in average.
Mask-Conditioned Planning
Standalone & differentiable: We can use DiPGrasp as a plug-and-play module for a pure perception model like Mask3D. When DiPGrasp is appended to Mask3D, it can directly conduct grasp planning on each segmented object point cloud, turning a 3D instance segmentation framework into a 3D dexterous grasp detection framework.
Pose Refinement
Differentiable: We can refine the predicted coarse grasp poses by appending DiPGrasp to a simple neural network regressor. Even the neural network prediction is extremely coarse in below case, we still can progressively make it a better grasp.
BibTeX
@article{dipgrasp,
title = {DiPGrasp: Parallel Local Searching for Efficient Differentiable Grasp Planning},
author = {Xu, Wenqiang and Zhang, Jieyi and Tang, Tutian and Yu, Zhenjun and Li, Yutong and Lu, Cewu},
journal = {IEEE Robotics and Automation Letters},
year = {2024},
publisher = {IEEE},
}