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 51–60 of 246 papers

Title	Date	Tasks	Status	Hype
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions	Sep 15, 2022	DecoderRobotic Grasping	CodeCode Available	1
Real-world multiobject, multigrasp detection	Oct 1, 2018	ObjectRobotic Grasping	CodeCode Available	1
Rethinking the Inception Architecture for Computer Vision	Dec 2, 2015	Computational EfficiencyImage Classification	CodeCode Available	1
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching	Oct 3, 2017	image-classificationImage Classification	CodeCode Available	1
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds	Oct 2, 2020	Imitation LearningMotion Planning	CodeCode Available	1
NBMOD: Find It and Grasp It in Noisy Background	Jun 17, 2023	Robotic Grasping	CodeCode Available	1
Depth-based 6DoF Object Pose Estimation using Swin Transformer	Mar 3, 2023	6D Pose Estimation6D Pose Estimation using RGB	CodeCode Available	1
Robotic Grasping of Harvested Tomato Trusses Using Vision and Online Learning	Sep 29, 2023	Robotic Grasping	—Unverified	0
DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Video	Feb 1, 2022	Deep Reinforcement LearningHuman-Object Interaction Detection	—Unverified	0
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images	Dec 7, 2021	DenoisingGrasp Generation	—Unverified	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