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 91–100 of 246 papers

Title	Date	Tasks	Status	Hype
Amodal Intra-class Instance Segmentation: Synthetic Datasets and Benchmark	Mar 12, 2023	Amodal Instance SegmentationInstance Segmentation	CodeCode Available	1
Simulation-based Bayesian inference for robotic grasping	Mar 10, 2023	Bayesian InferenceRobotic Grasping	—Unverified	0
Depth-based 6DoF Object Pose Estimation using Swin Transformer	Mar 3, 2023	6D Pose Estimation6D Pose Estimation using RGB	CodeCode Available	1
Perceiving Unseen 3D Objects by Poking the Objects	Feb 26, 2023	3D ReconstructionRobotic Grasping	—Unverified	0
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment	Feb 21, 2023	Deep Reinforcement LearningObject	—Unverified	0
Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications	Feb 12, 2023	3D Object TrackingObject	CodeCode Available	1
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction	Feb 7, 2023	Instance SegmentationMulti-Object Tracking	CodeCode Available	1
Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning	Jan 28, 2023	Data AugmentationRobotic Grasping	—Unverified	0
Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding	Jan 27, 2023	3D geometryRepresentation Learning	—Unverified	0
Learning to Generate All Feasible Actions	Jan 26, 2023	AllReinforcement Learning (RL)	—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