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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 161–170 of 246 papers

Title	Date	Tasks	Status	Hype
SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping	Mar 23, 2021	Robotic Grasping	—Unverified	0
DDGC: Generative Deep Dexterous Grasping in Clutter	Mar 8, 2021	Robotic Grasping	—Unverified	0
PointGuard: Provably Robust 3D Point Cloud Classification	Mar 4, 2021	3D Point Cloud ClassificationAutonomous Driving	—Unverified	0
Cooking Object's State Identification Without Using Pretrained Model	Mar 3, 2021	modelObject	—Unverified	0
Composing Pick-and-Place Tasks By Grounding Language	Feb 16, 2021	Natural Language Visual GroundingRobotic Grasping	CodeCode Available	0
Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection	Jan 25, 2021	object-detectionRobotic Grasping	—Unverified	0
Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic Grasping	Jan 18, 2021	Objectobject-detection	CodeCode Available	1
Action Priors for Large Action Spaces in Robotics	Jan 11, 2021	reinforcement-learningReinforcement Learning (RL)	CodeCode Available	0
Robotic Grasping of Fully-Occluded Objects using RF Perception	Dec 31, 2020	Deep Reinforcement LearningEfficient Exploration	—Unverified	0
Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial Settings	Dec 24, 2020	Deep LearningPose Estimation	CodeCode Available	0

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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