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 201–225 of 246 papers

TitleStatusHype
Research Challenges and Progress in Robotic Grasping and Manipulation Competitions—0
RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real—0
RoboGrasp: A Universal Grasping Policy for Robust Robotic Control—0
Robotic Grasp Detection using Deep Convolutional Neural Networks—0
Robotic Grasping of Fully-Occluded Objects using RF Perception—0
Robotic Grasp Manipulation Using Evolutionary Computing and Deep Reinforcement Learning—0
Robotic Handling of Compliant Food Objects by Robust Learning from Demonstration—0
Robotics and Computer-Integrated Manufacturing—0
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias—0
Robots Enact Malignant Stereotypes—0
Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement—0
MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object DetectionCode0
Learning Object Placements For Relational Instructions by Hallucinating Scene RepresentationsCode0
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping BaselineCode0
Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage TaskCode0
Jacquard: A Large Scale Dataset for Robotic Grasp DetectionCode0
Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in ClutterCode0
3D Semantic Segmentation of Modular Furniture using rjMCMCCode0
Shape-biased Texture Agnostic Representations for Improved Textureless and Metallic Object Detection and 6D Pose EstimationCode0
Composing Pick-and-Place Tasks By Grounding LanguageCode0
Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A ReviewCode0
Supertoroid fitting of objects with holes for robotic grasping and scene generationCode0
A Benchmarking Study of Vision-based Robotic Grasping AlgorithmsCode0
A Fast Method For Computing Principal Curvatures From Range ImagesCode0
Bimodal SegNet: Instance Segmentation Fusing Events and RGB Frames for Robotic GraspingCode0
Show:102550
← PrevPage 9 of 10Next →
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