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 76–100 of 246 papers

TitleStatusHype
Composing Pick-and-Place Tasks By Grounding LanguageCode0
State Representations as Incentives for Reinforcement Learning Agents: A Sim2Real Analysis on Robotic GraspingCode0
Solving the Real Robot Challenge using Deep Reinforcement LearningCode0
Shape-biased Texture Agnostic Representations for Improved Textureless and Metallic Object Detection and 6D Pose EstimationCode0
MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object DetectionCode0
A Fast Method For Computing Principal Curvatures From Range ImagesCode0
Learning Object Placements For Relational Instructions by Hallucinating Scene RepresentationsCode0
Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial SettingsCode0
Adversarial samples for deep monocular 6D object pose estimationCode0
Jacquard: A Large Scale Dataset for Robotic Grasp DetectionCode0
DynGraspVS: Servoing Aided Grasping for Dynamic EnvironmentsCode0
Bimodal SegNet: Instance Segmentation Fusing Events and RGB Frames for Robotic GraspingCode0
Improving Robot Dual-System Motor Learning with Intrinsically Motivated Meta-Control and Latent-Space Experience ImaginationCode0
HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language ModelsCode0
Action Priors for Large Action Spaces in RoboticsCode0
IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation TasksCode0
Supertoroid fitting of objects with holes for robotic grasping and scene generationCode0
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators—0
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
Densely Supervised Grasp Detector (DSGD)—0
Attribute-Based Robotic Grasping with Data-Efficient Adaptation—0
3DSGrasp: 3D Shape-Completion for Robotic Grasp—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