Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning

TL;DR

DISF tackles instability in surface-fitting grasp planning by explicitly incorporating contact stability through Center of Mass alignment. It disentangles grasp pose optimization into three sequential steps—Rotation Optimization for Contact Normal Misalignment ($RO$-$CNM$), Translation Refinement for CoM Alignment ($TR$-$CoMA$), and Fingertip Displacement Optimization for Stable Contact Distribution ($FDO$-$SCD$)—and employs a gradient-based least-squares solver with a small-rotation approximation. The method achieves an $80\%$ improvement in grasp success over conventional surface-fitting baselines on ten YCB objects while maintaining favorable geometric compatibility, demonstrating enhanced contact stability without sacrificing flexibility of surface fitting. These results suggest DISF can provide more robust, contact-stable grasps in varied, potentially unknown geometries, informing practical robotic grasping in real-world settings.

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/.

Figures (6)

• Figure 1: The relationship between the grasp planning space, geometrically aligned space, and spatially aligned space.
• Figure 2: Overview of the proposed DISF optimization process. The grasp pose optimization is disentangled into three sequential steps: (1) rotation optimization to align contact normals, (2) translation refinement for Center of Mass (CoM) alignment, and (3) gripper aperture adjustment to optimize contact point distribution. Each step iteratively updates the gripper transformation parameters to ensure both geometric compatibility and contact stability. The arrows indicate the optimization flow, illustrating how the gripper adapts to the object surface through iterative surface fitting.
• Figure 3: An image demonstrating how grasp planning can be reformulated as a contact surface optimization problem.
• Figure 4: The results of planned grasp quality. The Top part represents the geometric compatibility error, while the Bottom part represents the CoM alignment error. In both the Top and Bottom plots, lower values indicate better performance. For each object, the Top and Bottom correspond to the same method.
• Figure 5: This figure showcases successful grasp executions using the proposed DISF method. It presents grasp planning in point cloud space alongside simulation results. Key visual elements include contact points (cyan), surface fitting points (blue), and fingertip surfaces (plum and lime green). Normal vectors and hand axes are represented with arrows. The results confirm that DISF effectively optimizes grasp quality and achieves successful object grasping in simulation.