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 111–120 of 246 papers

Title	Date	Tasks	Status	Hype
6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic Grasping	Aug 30, 2022	6D Pose EstimationPose Estimation	CodeCode Available	1
MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object Detection	Aug 19, 2022	3D Object DetectionAutonomous Driving	CodeCode Available	0
Learning to Grasp on the Moon from 3D Octree Observations with Deep Reinforcement Learning	Aug 1, 2022	Deep Reinforcement LearningReinforcement Learning (RL)	—Unverified	0
Robots Enact Malignant Stereotypes	Jul 23, 2022	Bias DetectionGender Bias Detection	—Unverified	0
Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation	Jun 24, 2022	3D ReconstructionObject	—Unverified	0
Evaluating Gaussian Grasp Maps for Generative Grasping Models	Jun 1, 2022	ObjectRobotic Grasping	—Unverified	0
Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping	May 26, 2022	Computational EfficiencyDeep Reinforcement Learning	—Unverified	0
Open Arms: Open-Source Arms, Hands & Control	May 20, 2022	Robotic Grasping	—Unverified	0
Action Conditioned Tactile Prediction: case study on slip prediction	May 19, 2022	PredictionRobotic Grasping	CodeCode Available	1
Learning 6-DoF Object Poses to Grasp Category-level Objects by Language Instructions	May 9, 2022	ObjectObject Localization	—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