提出新方法提升抓取稳定性，让夹爪更稳地抓物。
DISF: Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning with Grasp Pose Alignment to the Object Center of Mass
- 分三步优化抓取姿态：对齐接触法向、对准质心、调整夹爪开合
- 仿真和实机测试中抓取成功率显著高于基线方法
- 适合需要稳定抓握的机器人操作场景
本文针对基于表面拟合的抓取规划方法仅关注夹爪与物体表面几何对齐而忽略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) 旋转优化以对齐接触法向，(2) 平移精调以提升夹爪原点与物体质心（CoM）的对齐度，(3) 夹爪开合调整以优化接触点分布。我们在15个物体上进行了仿真验证，涵盖已知形状（使用洁净CAD数据集）与观测形状（使用YCB物体数据集）两种设置，并在三个机器人-夹爪平台间实现跨平台抓取。进一步在UR3e机器人上进行真实世界抓取实验。结果表明，DISF在保持几何兼容性的同时显著降低质心偏移，相比基线方法提升了仿真与真实环境下的抓取成功率。更多视频与补充结果见项目页面：https://tomoya-yamanokuchi.github.io/disf-ras-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 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/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。