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 151–160 of 246 papers

Title	Date	Tasks	Status
GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping	Apr 14, 2022	Deep Reinforcement LearningRobotic Grasping	—Unverified
Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking	Apr 14, 2022	6D Pose Estimation using RGBPose Estimation	—Unverified
Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints	Mar 29, 2022	Graph Neural NetworkImage Augmentation	—Unverified
3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects	Mar 2, 2022	3D Object Reconstruction3D Reconstruction	—Unverified
Adversarial samples for deep monocular 6D object pose estimation	Mar 1, 2022	6D Pose Estimation6D Pose Estimation using RGB	CodeCode Available
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping Baseline	Feb 17, 2022	Depth CompletionRobotic Grasping	CodeCode Available
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition	Feb 10, 2022	reinforcement-learningReinforcement Learning	—Unverified
DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Video	Feb 1, 2022	Deep Reinforcement LearningHuman-Object Interaction Detection	—Unverified
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images	Dec 7, 2021	DenoisingGrasp Generation	—Unverified
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration	Dec 6, 2021	Few-Shot LearningHuman-Object Interaction Detection	—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