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 131–140 of 246 papers

Title	Date	Tasks	Status	Hype
DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Video	Feb 1, 2022	Deep Reinforcement LearningHuman-Object Interaction Detection	—Unverified	0
You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration	Jan 30, 2022	3D Object TrackingIndustrial Robots	CodeCode Available	2
MetaGraspNet_v0: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis	Dec 29, 2021	Objectobject-detection	CodeCode Available	1
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images	Dec 7, 2021	DenoisingGrasp Generation	—Unverified	0
CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks	Dec 6, 2021	Continuous ControlImitation Learning	CodeCode Available	1
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration	Dec 6, 2021	Few-Shot LearningHuman-Object Interaction Detection	—Unverified	0
Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks	Nov 22, 2021	Instance SegmentationObject	CodeCode Available	1
MPF6D: Masked Pyramid Fusion 6D Pose Estimation	Nov 17, 2021	6D Pose EstimationPose Estimation	—Unverified	0
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping	Nov 11, 2021	3D Object Recognition6D Pose Estimation	—Unverified	0
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer	Nov 8, 2021	Few-Shot LearningMeta Reinforcement Learning	CodeCode Available	1

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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