SOTAVerified

Robotic Grasping

This task is composed of using Deep Learning to identify how best to grasp objects using robotic arms in different scenarios. This is a very complex task as it might involve dynamic environments and objects unknown to the network.

Papers

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Showing 76–100 of 246 papers

TitleStatusHype
GaussianProperty: Integrating Physical Properties to 3D Gaussians with LMMs—0
Supertoroid fitting of objects with holes for robotic grasping and scene generationCode0
SparseGrasp: Robotic Grasping via 3D Semantic Gaussian Splatting from Sparse Multi-View RGB Images—0
An Adaptive Grasping Force Tracking Strategy for Nonlinear and Time-Varying Object Behaviors—0
Towards Cross-device and Training-free Robotic Grasping in 3D Open World—0
VMGNet: A Low Computational Complexity Robotic Grasping Network Based on VMamba with Multi-Scale Feature Fusion—0
Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent—0
ManiBox: Enhancing Spatial Grasping Generalization via Scalable Simulation Data Generation—0
Bayesian optimization for robust robotic grasping using a sensorized compliant hand—0
HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language ModelsCode0
From Words to Poses: Enhancing Novel Object Pose Estimation with Vision Language Models—0
Target-Oriented Object Grasping via Multimodal Human Guidance—0
LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion—0
DistillGrasp: Integrating Features Correlation with Knowledge Distillation for Depth Completion of Transparent Objects—0
Local Occupancy-Enhanced Object Grasping with Multiple Triplanar Projection—0
Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation—0
TARGO: Benchmarking Target-driven Object Grasping under Occlusions—0
Towards Open-World Grasping with Large Vision-Language Models—0
3D Feature Distillation with Object-Centric Priors—0
NeRF-Feat: 6D Object Pose Estimation using Feature Rendering—0
A Brief Survey on Leveraging Large Scale Vision Models for Enhanced Robot Grasping—0
Language-driven Grasp Detection—0
Towards Real-World Efficiency: Domain Randomization in Reinforcement Learning for Pre-Capture of Free-Floating Moving Targets by Autonomous RobotsCode0
You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objects—0
Robotics and Computer-Integrated Manufacturing—0
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All datasetsGraspNet-1BillionCornell Grasp DatasetJacquard dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FlexLoG-CDmAP56.02—Unverified
2GtG2.0mAP53.42—Unverified
3Scale-Balanced-Grasp-CDmAP48.97—Unverified
4graspness-CDmAP48.75—Unverified
5HGGD-CDmAP47.54—Unverified
6HGGDmAP44.24—Unverified
7graspnet-baseline-CDmAP35.45—Unverified
8graspnet-baselinemAP21.41—Unverified
#ModelMetricClaimedVerifiedStatus
1grasp_det_seg_cnn (rgb only, IW split)5 fold cross validation98.2—Unverified
2GR-ConvNet5 fold cross validation97.7—Unverified
3ResNet50 multi-grasp predictor5 fold cross validation96—Unverified
4Multi-Modal Grasp Predictor5 fold cross validation89.21—Unverified
5AlexNet, MultiGrasp5 fold cross validation88—Unverified
6GGCNN5 fold cross validation73—Unverified
7Fast Search5 fold cross validation60.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Efficient-GraspingAccuracy (%)95.6—Unverified
2GR-ConvNetAccuracy (%)94.6—Unverified
3grasp_det_seg_cnn (rgb only)Accuracy (%)92.95—Unverified