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

Lijingze Xiao; Jinhong Du; Supeng Diao; Yu Ren; Yang Cong

cs.ROMar 31, 2026

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

Authors: Lijingze Xiao, Jinhong Du, Supeng Diao, Yu Ren, Yang Cong

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.

Figures & tables

Fig. 1: Overview of the task pipeline. Under dense clutter and single-view observation constraints, the tabletop decluttering task is accomplished through a two-stage pipeline consisting of grasp candidate generation, grasp evaluation, and refinement.

Fig. 2: Convex superquadric shapes under different parameter settings.

Fig. 3: The primitive object database constructed in this work, which contains 1.2k standard geometric objects of various shapes and scales, including cylinders, frustums, elliptical cylinders, and cuboids.

Fig. 4: Overview: Our framework consists of two stages. In the first stage, we first estimate the superquadric coefficients of the target object and compute their similarity to those of the objects in the database. We then select the top- $N$ most similar objects and transfer their grasp poses to the target object through a transformation matrix $T$ , followed by a coarse filtering step to remove noisy candidates. In the second stage, we enlarge the grasp-aware region of each initial grasp and feed the cropped regional point cloud into a PointNet++ network for feature extraction. We then further crop the features corresponding to the points within the initial gripper closing region and aggregate them via max pooling to obtain the grasp anchor feature. Finally, the anchor feature is fed into an evaluation network and a refinement network to predict grasp feasibility and refinement feasibility, respectively.

Panel of Figure 5 — Fig. 5: Gripper configuration and refinement strategy. In (a), the $x$ -, $y$ -, and $z$ -axes define the gripper coordinate system, $w$ represents the gripper width, $L$ denotes the fingertip length, and $F_{d}$ is the applied disturbance force directed vertically downward. In (b), the initial gripper is first extended downward by $0.008\,\mathrm{m}$ along the $z$ -axis, and is then rotated by $\pm 15^{\circ}$ about the $z$ -axis.

Method	Seen(obj=10, scene=30)			Seen(obj=20, scene=30)			Unseen(obj=10, scene=30)
Method	GSR	TSR	GA	GSR	TSR	GA	GSR	TSR	GA
PointNetGPD [ 7 ]	78.76%	29.67%	113	59.43%	34.67%	350	33.90%	6.67%	59
Contact-GraspNet [ 16 ]	78.25%	80.33%	308	75.42%	82.83%	659	63.91%	69.67%	327
Chen et al. [ 5 ]	–	–	–	–	–	–	58.14%	83.33%	430
Ours (S1) + PointNetGPD [ 7 ]	63.34%	72.00%	341	38.42%	47.83%	747	31.62%	37.00%	351
PointNetGPD [ 7 ] + Ours (S2)	93.60%	53.67%	172	79.02%	48.33%	367	87.85%	31.33%	107

TABLE I: Comparison of different methods in simulation

Fig. 6: Simulation experiment settings. (a) Scene with 10 seen objects. (b) Scene with 20 seen objects. (c) Scene with 10 novel objects.

Fig. 7: Real-world experiment settings. We design two real-world test scenarios: (1) the objects within the green box are similar to those in the training set; and (2) the objects within the red box differ significantly from the training set in both shape and size.

Method	Similar (obj=10, scene=10)		Novel (obj=10, scene=10)
Method	GSR	TSR	GSR	TSR
PointNetGPD [ 7 ]	58.82%	20.00%	52.38%	11.00%
Contact-GraspNet [ 16 ]	78.95%	60.00%	74.65%	53.00%
Chen et al. [ 5 ]	–	–	69.23%	90.00%
Ours	98.00%	98.00%	94.12%	96.00%

TABLE II: Comparison of different methods in the real world

Method	Stage-1 (s)	Stage-2 (s)	Total (s)
PointNetGPD [ 7 ]	9.276	0.732	10.008
Contact-GraspNet [ 16 ]	–	–	0.333
Chen et al. [ 5 ]	0.509	1.484	1.993
Ours	0.346	0.367	0.713

TABLE III: Time cost of the grasping process

Superquadric Representation

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Mar 16, 2026J/K move · Enter open · S save

DGS-Net: Learning Dexterous Grasps from Single-View Point Clouds via a Multi-Object Scene Dataset

Tao Geng, Dapeng Yang, Ziwei Liu, Le Zhang+5

Robotic Grasping Synthetic Data Generation for Robotics

Open paper AskPDF arXiv

Dexterous grasping in multi-object scene constitutes a fundamental challenge in robotic manipulation. Current mainstream grasping datasets predominantly focus on single-object scenarios and predefined grasp configurations, often neglecting environmental interference and the modeling of dexterous pre-grasp gesture, thereby limiting their generalizability in real-world applications. To address this, we propose DGS-Net, an end-to-end grasp prediction network capable of learning dense grasp configurations from single-view point clouds in multi-object scene. Furthermore, we propose a two-stage grasp data generation strategy that progresses from dense single-object grasp synthesis to dense scene-level grasp generation. Our dataset comprises 307 objects, 240 multi-object scenes, and over 350k validated grasps. By explicitly modeling grasp offsets and pre-grasp configurations, the dataset provides more robust and accurate supervision for dexterous grasp learning. Experimental results show that DGS-Net achieves grasp success rates of 88.63% in simulation and 78.98% on a real robotic platform, while exhibiting lower penetration with a mean penetration depth of 0.375 mm and penetration volume of 559.45 mm^3, outperforming existing methods and demonstrating strong effectiveness and generalization capability. Our dataset is available at https://github.com/4taotao8/DGS-Net.

Abstract

Superquadric Representation

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