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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 246 papers

TitleStatusHype
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-ResolutionsCode1
Measure Anything: Real-time, Multi-stage Vision-based Dimensional Measurement using Segment AnythingCode1
Event-based Robotic Grasping Detection with Neuromorphic Vision Sensor and Event-Stream DatasetCode1
6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and BenchmarkCode1
Grasping Field: Learning Implicit Representations for Human GraspsCode1
Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map RepresentationCode1
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot InteractionCode1
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
FViT-Grasp: Grasping Objects With Using Fast Vision Transformers—0
GAA-TSO: Geometry-Aware Assisted Depth Completion for Transparent and Specular Objects—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
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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