# SuperGrasp：通过超二次曲面相似性匹配、评估与细化实现单视角物体抓取

> 原文链接：https://robotics.hellobike.com/papers/2026-10-08-2603-29254
> 频道：papers | 更新日期：2026-10-08 | 标签：arxiv, cs.RO
> 核心摘要：SuperGrasp面向单视角平行夹爪抓取，通过超二次曲面相似匹配与E-RNet评估细化，在仿真和真实环境验证。

# SuperGrasp：通过超二次曲面相似性匹配、评估与细化实现单视角物体抓取

::: v-pre
> 原文链接：[SuperGrasp: Single-View Object Grasping via Superquadric Similarity Matching, Evaluation, and Refinement](https://arxiv.org/abs/2603.29254)（arXiv 2603.29254 · 发布日期：2026-10-08）

**SuperGrasp: Single-View Object Grasping via Superquadric Similarity Matching, Evaluation, and Refinement**

SuperGrasp面向单视角平行夹爪抓取，通过超二次曲面相似匹配与E-RNet评估细化，在仿真和真实环境验证。

## 核心要点

- 单视角几何信息不完整，使现有方法难以生成可靠抓取候选并稳定评估可行性。
- 框架先基于超二次曲面系数匹配单视角点云与预计算基元，检索有效且多样的候选。
- E-RNet扩展抓取感知区域，以闭合区域为锚点建模邻域关系，用于评估并小范围细化。
- 构建1.2k标准几何基元和100k点云样本；仿真与真实实验显示稳定抓取和泛化能力。

**验证范围与局限**：验证范围为仿真与真实环境中的新物体和杂乱场景单视角平行夹爪抓取。

## 英文摘要 (Abstract)

Robotic grasping from single-view observations remains a critical challenge in manipulation. However, existing methods still struggle to generate reliable grasp candidates and stably evaluate grasp feasibility under incomplete geometric information. To address these limitations, we present SuperGrasp, a new two-stage framework for single-view parallel-jaw grasping. In the first stage, we introduce a Similarity Matching Module that efficiently retrieves valid and diverse grasp candidates by matching the input single-view point cloud with a precomputed primitive dataset based on superquadric coefficients. In the second stage, we propose E-RNet, an end-to-end network that expands the grasp-aware region and takes the initial grasp closure region as a local anchor region, capturing the contextual relationship between the local region and its surrounding spatial neighborhood, thereby enabling more accurate and reliable grasp evaluation and introducing small-range local refinement to improve grasp adaptability. To enhance generalization, we construct a primitive dataset containing 1.2k standard geometric primitives for similarity matching and collect a point cloud dataset of 100k samples from 124 objects, annotated with stable grasp labels for network training. Extensive experiments in both simulation and real-world environments demonstrate that our method achieves stable grasping performance and good generalization across novel objects and clutter scenes.

作者：Lijingze Xiao, Jinhong Du, Supeng Diao, Yu Ren, Yang Cong
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## 原始参考来源 (Sources)

- [arXiv 2603.29254：SuperGrasp: Single-View Object Grasping via Superquadric Similarity Matching, Evaluation, and Refinement](https://arxiv.org/abs/2603.29254)