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 201–246 of 246 papers

TitleStatusHype
Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures—0
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping—0
Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in ClutterCode0
Deep Robotic Prediction with hierarchical RGB-D Fusion—0
Antipodal Robotic Grasping using Generative Residual Convolutional Neural NetworkCode0
Towards Precise Robotic Grasping by Probabilistic Post-grasp Displacement Estimation—0
Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks—0
PyRobot: An Open-source Robotics Framework for Research and BenchmarkingCode1
Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A ReviewCode0
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping—0
Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes—0
The RobotriX: An eXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and InteractionsCode0
3D Convolution on RGB-D Point Clouds for Accurate Model-free Object Pose Estimation—0
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module—0
Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks—0
Dealing with Ambiguity in Robotic Grasping via Multiple Predictions—0
The CoSTAR Block Stacking Dataset: Learning with Workspace ConstraintsCode0
Real-world multiobject, multigrasp detectionCode1
Densely Supervised Grasp Detector (DSGD)—0
FastOrient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping—0
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias—0
Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images—0
Learning to Grasp from a Single Demonstration—0
More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch—0
Jacquard: A Large Scale Dataset for Robotic Grasp DetectionCode0
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy MethodsCode0
Edge-Based Recognition of Novel Objects for Robotic Grasping—0
Domain Randomization and Generative Models for Robotic Grasping—0
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?Code0
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image MatchingCode1
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic GraspingCode1
AirCode: Unobtrusive Physical Tags for Digital Fabrication—0
Transferring End-to-End Visuomotor Control from Simulation to Real World for a Multi-Stage TaskCode0
End-to-End Learning of Semantic Grasping—0
A Fast Method For Computing Principal Curvatures From Range ImagesCode0
Learning a visuomotor controller for real world robotic grasping using simulated depth images—0
3D Semantic Segmentation of Modular Furniture using rjMCMCCode0
An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning—0
Robotic Grasp Detection using Deep Convolutional Neural Networks—0
Latest Datasets and Technologies Presented in the Workshop on Grasping and Manipulation Datasets—0
Fast Graph-Based Object Segmentation for RGB-D Images—0
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection—0
Rethinking the Inception Architecture for Computer VisionCode1
Real-Time Grasp Detection Using Convolutional Neural NetworksCode1
Deep Learning for Detecting Robotic Grasps—0
Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models—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