Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning

TL;DR

This paper introduces a novel surface fitting algorithm for grasp planning that enhances contact stability by disentangling pose optimization into rotation, translation, and aperture adjustments, leading to more stable grasps.

Contribution

The work presents a new iterative surface fitting method that explicitly incorporates contact stability into grasp planning, inspired by human grasping behavior.

Findings

80% improvement in grasp success rate over traditional methods

Validated on ten YCB objects in simulation

Effective in optimizing contact point distribution and stability

Abstract

In this work, we address the limitation of surface fitting-based grasp planning algorithm, which primarily focuses on geometric alignment between the gripper and object surface while overlooking the stability of contact point distribution, often resulting in unstable grasps due to inadequate contact configurations. To overcome this limitation, we propose a novel surface fitting algorithm that integrates contact stability while preserving geometric compatibility. Inspired by human grasping behavior, our method disentangles the grasp pose optimization into three sequential steps: (1) rotation optimization to align contact normals, (2) translation refinement to improve Center of Mass (CoM) alignment, and (3) gripper aperture adjustment to optimize contact point distribution. We validate our approach through simulations on ten YCB dataset objects, demonstrating an 80% improvement in grasp success over conventional surface fitting methods that disregard contact stability. Further details can be found on our project page: https://tomoya-yamanokuchi.github.io/disf-project-page/.