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 151–200 of 246 papers

TitleStatusHype
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
Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation—0
Collision-Aware Target-Driven Object Grasping in Constrained Environments—0
Cooking Object's State Identification Without Using Pretrained Model—0
Corner-Grasp: Multi-Action Grasp Detection and Active Gripper Adaptation for Grasping in Cluttered Environments—0
Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks—0
DDGC: Generative Deep Dexterous Grasping in Clutter—0
Dealing with Ambiguity in Robotic Grasping via Multiple Predictions—0
Deep Learning for Detecting Robotic Grasps—0
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment—0
Deep Robotic Prediction with hierarchical RGB-D Fusion—0
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration—0
Densely Supervised Grasp Detector (DSGD)—0
DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Video—0
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators—0
DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in Cluttered Scenes—0
Domain Randomization and Generative Models for Robotic Grasping—0
Domestic waste detection and grasping points for robotic picking up—0
Edge-Based Recognition of Novel Objects for Robotic Grasping—0
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning—0
Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation—0
Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation—0
Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space—0
End-to-End Learning of Semantic Grasping—0
Evaluating Gaussian Grasp Maps for Generative Grasping Models—0
Fast Graph-Based Object Segmentation for RGB-D Images—0
Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge—0
FastOrient: Lightweight Computer Vision for Wrist Control in Assistive Robotic 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