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 161–170 of 246 papers

Title	Date	Tasks	Status
MPF6D: Masked Pyramid Fusion 6D Pose Estimation	Nov 17, 2021	6D Pose EstimationPose Estimation	—Unverified
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
When Neural Networks Using Different Sensors Create Similar Features	Nov 4, 2021	Autonomous DrivingClassification	—Unverified
Validate on Sim, Detect on Real -- Model Selection for Domain Randomization	Nov 1, 2021	Model SelectionOut-of-Distribution Detection	—Unverified
Solving the Real Robot Challenge using Deep Reinforcement Learning	Sep 30, 2021	Deep Reinforcement Learningreinforcement-learning	CodeCode Available
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition	Sep 29, 2021	reinforcement-learningReinforcement Learning	—Unverified
Simulation-based Bayesian inference for multi-fingered robotic grasping	Sep 29, 2021	Bayesian InferenceRobotic Grasping	—Unverified
Sample-Efficient Safety Assurances using Conformal Prediction	Sep 28, 2021	Conformal PredictionPrediction	—Unverified
Robust Extrinsic Symmetry Estimation in 3D Point Clouds	Sep 21, 2021	object-detectionObject Detection	—Unverified
ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations	Sep 16, 2021	3D ReconstructionObject	—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