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 201–225 of 246 papers

TitleStatusHype
Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge—0
FastOrient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping—0
From Words to Poses: Enhancing Novel Object Pose Estimation with Vision Language Models—0
FViT-Grasp: Grasping Objects With Using Fast Vision Transformers—0
GAA-TSO: Geometry-Aware Assisted Depth Completion for Transparent and Specular Objects—0
GaussianProperty: Integrating Physical Properties to 3D Gaussians with LMMs—0
GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping—0
GP-net: Flexible Viewpoint Grasp Proposal—0
Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent—0
Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting—0
Grasping Trajectory Optimization with Point Clouds—0
Grasp the Graph (GtG) 2.0: Ensemble of GNNs for High-Precision Grasp Pose Detection in Clutter—0
Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction—0
HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation—0
Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping—0
Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss—0
Instance segmentation based 6D pose estimation of industrial objects using point clouds for robotic bin-picking—0
Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation—0
Jacquard V2: Refining Datasets using the Human In the Loop Data Correction Method—0
JENGA: Object selection and pose estimation for robotic grasping from a stack—0
Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures—0
Language-driven Grasp Detection—0
Latest Datasets and Technologies Presented in the Workshop on Grasping and Manipulation Datasets—0
Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding—0
Learning 6-DoF Object Poses to Grasp Category-level Objects by Language Instructions—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