Planning of Power Grasps Using Infinite Program Under Complementary Constraints
This work addresses the problem of robust robotic grasping for manipulation tasks, representing an incremental improvement through a novel optimization formulation.
The authors tackled the problem of planning power grasps for robotic hands by formulating it as an infinite program under complementary constraints (IPCC), which they reduced to a finite-dimensional nonlinear program using kernel-integral relaxation and solved efficiently with a modified Fast Multipole Method. Their method demonstrated superior grasp quality on challenging 3D objects and high-DOF grippers like Barrett Hand and Shadow Hand compared to competitors.
We propose an optimization-based approach to plan power grasps. Central to our method is a reformulation of grasp planning as an infinite program under complementary constraints (IPCC), which allows contacts to happen between arbitrary pairs of points on the object and the robot gripper. We show that IPCC can be reduced to a conventional finite-dimensional nonlinear program (NLP) using a kernel-integral relaxation. Moreover, the values and Jacobian matrices of the kernel-integral can be evaluated efficiently using a modified Fast Multipole Method (FMM). We further guarantee that the planned grasps are collision-free using primal barrier penalties. We demonstrate the effectiveness, robustness, and efficiency of our grasp planner on a row of challenging 3D objects and high-DOF grippers, such as Barrett Hand and Shadow Hand, where our method achieves superior grasp qualities over competitors.