MANGO-Grasp：面向几何的三维高斯模型上的马氏场，用于跨 embodiments 的灵巧抓取

中文摘要 AI 辅助

跨 embodiments 的灵巧抓取旨在合成稳定的抓取姿态，适用于异构多指手，且只需极少或无需特定于该 embodiment 的调整。现有的以交互为中心的方法取得了不错的结果，但它们的物体表示往往对局部表面几何的表征不足，而机器人描述符也未明确编码机器人的形态和运动学信息。我们提出了MANGO-Grasp，这是一种各向异性交互框架，它将物体表示为面向几何的三维高斯基元，将机器人手表示为编码为形态运动学描述符的表面关键点。这些物体基元根据几何复杂度自适应分配，被塑造成与表面对齐的板状结构，带有外法线，用于编码局部几何信息。关键点-基元对上的马氏场在训练时作为交互预测目标，在推理时作为抓取实现的优化指导。这些场沿表面法线方向的位移会急剧上升，而在切平面内仅缓慢变化，与接触的方向结构相匹配。所有 embodiments 都采用相同的优化公式和超参数设置来实现抓取。在CMAP和MultiGripperGrasp基准测试中，MANGO-Grasp在仿真中比最强的见过手基准高出最多8.24个百分点，还能零样本迁移到未见过的SharpaWave手，比最强的零样本基准高出最多16.57个百分点，在真实实验中达到86%的成功率。代码和额外材料将在发表后在此httpsURL提供。

英文摘要

Cross-embodiment dexterous grasping aims to synthesize stable grasps across heterogeneous multi-fingered hands with little or no embodiment-specific tuning. Existing interaction-centric methods achieve promising results, but their object representations often underrepresent local surface geometry, while their robot descriptors do not explicitly encode both robot morphology and kinematics. We propose MANGO-Grasp, an anisotropic interaction framework that represents objects as geometry-oriented 3D Gaussian primitives and robot hands as surface keypoints encoded into morpho-kinematic descriptors. The object primitives are adaptively allocated by geometric complexity and shaped as surface-aligned plates with outward normals, encoding local geometry. Mahalanobis fields over keypoint--primitive pairs serve as interaction prediction targets during training and as optimization guidance for grasp realization at inference. These fields rise sharply for displacement along the surface normal but only gently within the tangent plane, matching the directional structure of contact. Grasps are realized with one shared optimization formulation and hyperparameter setting across all embodiments. On the CMAP and MultiGripperGrasp benchmarks, MANGO-Grasp outperforms the strongest seen-hand baseline by up to 8.24 percentage points in simulation. It also transfers zero-shot to the unseen SharpaWave hand, improving over the strongest zero-shot baseline by up to 16.57 percentage points, and achieves 86% success in real-world experiments. The code and additional materials will be made available upon publication at https://connor-zh.github.io/MANGO-Grasp/.

发表机构

• College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
• National University of Singapore（新加坡国立大学）

机构由 AI 辅助整理，请以论文原文为准。