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 101–125 of 246 papers

TitleStatusHype
Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes—0
PointGuard: Provably Robust 3D Point Cloud Classification—0
Control of the Final-Phase of Closed-Loop Visual Grasping using Image-Based Visual Servoing—0
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping—0
Real-Time Fruit Recognition and Grasping Estimation for Autonomous Apple Harvesting—0
Reinforcement Learning-Based Bionic Reflex Control for Anthropomorphic Robotic Grasping exploiting Domain Randomization—0
Research Challenges and Progress in Robotic Grasping and Manipulation Competitions—0
RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real—0
RoboGrasp: A Universal Grasping Policy for Robust Robotic Control—0
Robotic Grasp Detection using Deep Convolutional Neural Networks—0
Robotic Grasping of Fully-Occluded Objects using RF Perception—0
Robotic Grasp Manipulation Using Evolutionary Computing and Deep Reinforcement Learning—0
Robotic Handling of Compliant Food Objects by Robust Learning from Demonstration—0
Robotics and Computer-Integrated Manufacturing—0
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias—0
Robots Enact Malignant Stereotypes—0
Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement—0
Robust Extrinsic Symmetry Estimation in 3D Point Clouds—0
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module—0
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition—0
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition—0
SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects—0
Sample-Efficient Safety Assurances using Conformal Prediction—0
Self-Supervised Instance Segmentation by Grasping—0
ShapeShift: Superquadric-based Object Pose Estimation for 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