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 51–100 of 246 papers

TitleStatusHype
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-ResolutionsCode1
Real-world multiobject, multigrasp detectionCode1
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven OptimizationCode1
6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and BenchmarkCode1
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot InteractionCode1
Towards Scale Balanced 6-DoF Grasp Detection in Cluttered ScenesCode1
Depth-based 6DoF Object Pose Estimation using Swin TransformerCode1
Robotic Grasping of Harvested Tomato Trusses Using Vision and Online Learning—0
DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Video—0
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images—0
Attribute-Based Robotic Grasping with One-Grasp Adaptation—0
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators—0
Densely Supervised Grasp Detector (DSGD)—0
Attribute-Based Robotic Grasping with Data-Efficient Adaptation—0
3DSGrasp: 3D Shape-Completion for Robotic Grasp—0
DistillGrasp: Integrating Features Correlation with Knowledge Distillation for Depth Completion of Transparent Objects—0
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration—0
Deep Robotic Prediction with hierarchical RGB-D Fusion—0
Acceleration of Actor-Critic Deep Reinforcement Learning for Visual Grasping in Clutter by State Representation Learning Based on Disentanglement of a Raw Input Image—0
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment—0
Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures—0
Language-driven Grasp Detection—0
Deep Learning for Detecting Robotic Grasps—0
Dealing with Ambiguity in Robotic Grasping via Multiple Predictions—0
A Brief Survey on Leveraging Large Scale Vision Models for Enhanced Robot Grasping—0
DDGC: Generative Deep Dexterous Grasping in Clutter—0
Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks—0
An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning—0
3D Convolution on RGB-D Point Clouds for Accurate Model-free Object Pose Estimation—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
Corner-Grasp: Multi-Action Grasp Detection and Active Gripper Adaptation for Grasping in Cluttered Environments—0
Cooking Object's State Identification Without Using Pretrained Model—0
An Adaptive Grasping Force Tracking Strategy for Nonlinear and Time-Varying Object Behaviors—0
GaussianProperty: Integrating Physical Properties to 3D Gaussians with LMMs—0
3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects—0
Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation—0
GAA-TSO: Geometry-Aware Assisted Depth Completion for Transparent and Specular Objects—0
FViT-Grasp: Grasping Objects With Using Fast Vision Transformers—0
GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping—0
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping—0
From Words to Poses: Enhancing Novel Object Pose Estimation with Vision Language Models—0
GP-net: Flexible Viewpoint Grasp Proposal—0
Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent—0
LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion—0
FastOrient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping—0
Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting—0
Grasping Trajectory Optimization with Point Clouds—0
Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge—0
Collision-Aware Target-Driven Object Grasping in Constrained Environments—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