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 101–110 of 246 papers

Title	Date	Tasks	Status
Deep Learning for Detecting Robotic Grasps	Jan 16, 2013	Deep LearningRobotic Grasping	—Unverified
Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction	Aug 2, 2020	ObjectOptical Flow Estimation	—Unverified
Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge	Apr 21, 2023	Multi-Task LearningRobotic Grasping	—Unverified
HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation	Apr 20, 2022	6D Pose Estimation6D Pose Estimation using RGB	—Unverified
Collision-Aware Target-Driven Object Grasping in Constrained Environments	Apr 1, 2021	ObjectRobotic Grasping	—Unverified
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment	Feb 21, 2023	Deep Reinforcement LearningObject	—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
Fast Graph-Based Object Segmentation for RGB-D Images	May 12, 2016	ObjectRobotic Grasping	—Unverified
Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation	Jul 17, 2024	Dataset GenerationDeep Learning	—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