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 151–175 of 246 papers

TitleStatusHype
ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations—0
Research Challenges and Progress in Robotic Grasping and Manipulation Competitions—0
End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGBCode1
Domestic waste detection and grasping points for robotic picking up—0
REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in ClutterCode1
Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation—0
Attribute-Based Robotic Grasping with One-Grasp Adaptation—0
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic GraspingCode0
Collision-Aware Target-Driven Object Grasping in Constrained Environments—0
Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered ScenesCode1
SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping—0
DDGC: Generative Deep Dexterous Grasping in Clutter—0
PointGuard: Provably Robust 3D Point Cloud Classification—0
Cooking Object's State Identification Without Using Pretrained Model—0
Composing Pick-and-Place Tasks By Grounding LanguageCode0
Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection—0
Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic GraspingCode1
Action Priors for Large Action Spaces in RoboticsCode0
Robotic Grasping of Fully-Occluded Objects using RF Perception—0
Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial SettingsCode0
Object SLAM-Based Active Mapping and Robotic GraspingCode1
Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented GraspingCode1
Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss—0
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point CloudsCode1
6-DoF Grasp Planning using Fast 3D Reconstruction and Grasp Quality CNN—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