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
When Neural Networks Using Different Sensors Create Similar Features—0
You Only Estimate Once: Unified, One-stage, Real-Time Category-level Articulated Object 6D Pose Estimation for Robotic Grasping—0
You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objects—0
ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping—0
ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations—0
LAC-Net: Linear-Fusion Attention-Guided Convolutional Network for Accurate Robotic Grasping Under the Occlusion—0
3D Convolution on RGB-D Point Clouds for Accurate Model-free Object Pose Estimation—0
3D Feature Distillation with Object-Centric Priors—0
3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects—0
3DSGrasp: 3D Shape-Completion for Robotic Grasp—0
6-DoF Grasp Planning using Fast 3D Reconstruction and Grasp Quality CNN—0
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping—0
A Brief Survey on Leveraging Large Scale Vision Models for Enhanced Robot Grasping—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
AGILE: Approach-based Grasp Inference Learned from Element Decomposition—0
AirCode: Unobtrusive Physical Tags for Digital Fabrication—0
An Adaptive Grasping Force Tracking Strategy for Nonlinear and Time-Varying Object Behaviors—0
An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning—0
Attribute-Based Robotic Grasping with Data-Efficient Adaptation—0
Attribute-Based Robotic Grasping with One-Grasp Adaptation—0
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images—0
Robotic Grasping of Harvested Tomato Trusses Using Vision and Online Learning—0
Bayesian optimization for robust robotic grasping using a sensorized compliant hand—0
Category-Level 6D Object Pose Estimation in Agricultural Settings Using a Lattice-Deformation Framework and Diffusion-Augmented Synthetic Data—0
Category-Level and Open-Set Object Pose Estimation for Robotics—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