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 211–220 of 246 papers

Title	Date	Tasks	Status
Grasping Trajectory Optimization with Point Clouds	Mar 8, 2024	Collision AvoidanceRobotic Grasping	—Unverified
Grasp the Graph (GtG) 2.0: Ensemble of GNNs for High-Precision Grasp Pose Detection in Clutter	May 5, 2025	Graph Neural NetworkRobotic Grasping	—Unverified
Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction	Aug 2, 2020	ObjectOptical Flow Estimation	—Unverified
HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation	Apr 20, 2022	6D Pose Estimation6D Pose Estimation using RGB	—Unverified
Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping	Apr 18, 2023	Bayesian InferenceRobotic Grasping	—Unverified
Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss	Nov 5, 2020	Multi-Task LearningRobotic Grasping	—Unverified
Instance segmentation based 6D pose estimation of industrial objects using point clouds for robotic bin-picking	Aug 10, 2023	6D Pose EstimationInstance Segmentation	—Unverified
Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation	Apr 15, 2021	6D Pose Estimation using RGBObject	—Unverified
Jacquard V2: Refining Datasets using the Human In the Loop Data Correction Method	Feb 8, 2024	Robotic Grasping	—Unverified
JENGA: Object selection and pose estimation for robotic grasping from a stack	Jun 16, 2025	BenchmarkingObject	—Unverified

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All datasets GraspNet-1Billion Cornell Grasp Dataset Jacquard dataset

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	FlexLoG-CD	mAP	56.02	—	Unverified
2	GtG2.0	mAP	53.42	—	Unverified
3	Scale-Balanced-Grasp-CD	mAP	48.97	—	Unverified
4	graspness-CD	mAP	48.75	—	Unverified
5	HGGD-CD	mAP	47.54	—	Unverified
6	HGGD	mAP	44.24	—	Unverified
7	graspnet-baseline-CD	mAP	35.45	—	Unverified
8	graspnet-baseline	mAP	21.41	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	grasp_det_seg_cnn (rgb only, IW split)	5 fold cross validation	98.2	—	Unverified
2	GR-ConvNet	5 fold cross validation	97.7	—	Unverified
3	ResNet50 multi-grasp predictor	5 fold cross validation	96	—	Unverified
4	Multi-Modal Grasp Predictor	5 fold cross validation	89.21	—	Unverified
5	AlexNet, MultiGrasp	5 fold cross validation	88	—	Unverified
6	GGCNN	5 fold cross validation	73	—	Unverified
7	Fast Search	5 fold cross validation	60.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Efficient-Grasping	Accuracy (%)	95.6	—	Unverified
2	GR-ConvNet	Accuracy (%)	94.6	—	Unverified
3	grasp_det_seg_cnn (rgb only)	Accuracy (%)	92.95	—	Unverified