新算法分步优化抓取姿态，兼顾几何对齐与接触稳定，提升抓握成功率。
Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning
- 分三步优化：旋转对齐法向、平移对齐质心、调整开合优化接触点分布
- 在YCB数据集上比传统方法抓取成功率达80%提升
- 适合需要稳定接触的机器人抓取任务，如精密操作
本文针对基于表面拟合的抓取规划算法仅关注夹爪与物体表面几何对齐而忽视接触点分布稳定性的问题，提出一种融合接触稳定性的新型表面拟合算法。受人类抓握行为启发，该方法将抓取姿态优化分解为三个连续步骤：(1) 通过旋转优化对齐接触法向，(2) 通过平移精修改善质心（CoM）对齐，(3) 通过调整夹爪开合度优化接触点分布。我们在十种YCB数据集物体上进行仿真验证，结果表明该方法相比忽略接触稳定性的传统表面拟合方法，抓取成功率提升80%。更多细节见项目页面：https://tomoya-yamanokuchi.github.io/disf-project-page/。
原文摘要 · Abstract (English)
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/.
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