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 the alignment between the gripper frame origin and the object Center of Mass (CoM), and (3) gripper aperture adjustment to optimize contact point distribution. We validate our approach in simulation across 15 objects under both Known-shape (with clean CAD-derived dataset) and Observed-shape (with YCB object dataset) settings, including cross-platform grasp execution on three robot--gripper platforms. We further validate the method in real-world grasp experiments on a UR3e robot. Overall, DISF reduces CoM misalignment while maintaining geometric compatibility, translating into higher grasp success in both simulation and real-world execution compared to baselines. Additional videos and supplementary results are available on our project page: https://tomoya-yamanokuchi.github.io/disf-ras-project-page/

翻译：本研究针对现有基于曲面拟合的抓取规划算法主要关注夹持器与物体表面的几何对齐，而忽视接触点分布的稳定性，常因接触配置不当导致抓取不稳定的局限性。为克服此局限，我们提出一种新颖的曲面拟合算法，在保持几何兼容性的同时整合接触稳定性。受人类抓取行为启发，我们的方法将抓取位姿优化解耦为三个顺序步骤：(1) 通过旋转优化对齐接触法线，(2) 通过平移细化改进夹持器坐标系原点与物体质心(Center of Mass, CoM)的对齐，(3) 通过夹持器开度调整优化接触点分布。我们在仿真环境中对15个物体进行了验证，涵盖已知形状（使用洁净CAD衍生数据集）和观测形状（使用YCB物体数据集）两种设置，包括在三个机器人-夹持器平台上的跨平台抓取执行。我们进一步在UR3e机器人上进行了真实世界抓取实验验证。总体而言，与基线方法相比，DISF在保持几何兼容性的同时减少了质心错位，从而在仿真和真实世界执行中实现了更高的抓取成功率。更多视频和补充结果详见项目页面：https://tomoya-yamanokuchi.github.io/disf-ras-project-page/