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
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration	Dec 6, 2021	Few-Shot LearningHuman-Object Interaction Detection	—Unverified
Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss	Nov 5, 2020	Multi-Task LearningRobotic Grasping	—Unverified
Deep Robotic Prediction with hierarchical RGB-D Fusion	Sep 14, 2019	DecoderDepth Estimation	—Unverified
Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping	Apr 18, 2023	Bayesian InferenceRobotic 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
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment	Feb 21, 2023	Deep Reinforcement LearningObject	—Unverified
Acceleration of Actor-Critic Deep Reinforcement Learning for Visual Grasping in Clutter by State Representation Learning Based on Disentanglement of a Raw Input Image	Feb 27, 2020	Deep Reinforcement LearningDisentanglement	—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
Hindsight for Foresight: Unsupervised Structured Dynamics Models from Physical Interaction	Aug 2, 2020	ObjectOptical Flow Estimation	—Unverified
Deep Learning for Detecting Robotic Grasps	Jan 16, 2013	Deep LearningRobotic Grasping	—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