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 201–225 of 246 papers

TitleStatusHype
Robotic Grasp Manipulation Using Evolutionary Computing and Deep Reinforcement Learning—0
Reward Engineering for Object Pick and Place TrainingCode0
IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation TasksCode0
Self-supervised 3D Shape and Viewpoint Estimation from Single Images for RoboticsCode0
Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space—0
Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors—0
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
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
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
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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