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 201–246 of 246 papers

TitleStatusHype
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
Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering—0
Learning a visuomotor controller for real world robotic grasping using simulated depth images—0
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection—0
Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint—0
Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images—0
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping—0
Learning to Generate All Feasible Actions—0
Learning to Grasp from a Single Demonstration—0
Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints—0
Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection—0
Local Occupancy-Enhanced Object Grasping with Multiple Triplanar Projection—0
ManiBox: Enhancing Spatial Grasping Generalization via Scalable Simulation Data Generation—0
MonoGraspNet: 6-DoF Grasping with a Single RGB Image—0
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch—0
MPF6D: Masked Pyramid Fusion 6D Pose Estimation—0
MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping—0
Natural Language Robot Programming: NLP integrated with autonomous robotic grasping—0
NeRF-Feat: 6D Object Pose Estimation using Feature Rendering—0
NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis—0
NeuGrasp: Generalizable Neural Surface Reconstruction with Background Priors for Material-Agnostic Object Grasp Detection—0
One-Shot Neural Fields for 3D Object Understanding—0
Neuromorphic Event-Based Slip Detection and suppression in Robotic Grasping and Manipulation—0
DistillGrasp: Integrating Features Correlation with Knowledge Distillation for Depth Completion of Transparent Objects—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