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 141–150 of 246 papers

Title	Date	Tasks	Status	Hype
When Neural Networks Using Different Sensors Create Similar Features	Nov 4, 2021	Autonomous DrivingClassification	—Unverified	0
Validate on Sim, Detect on Real -- Model Selection for Domain Randomization	Nov 1, 2021	Model SelectionOut-of-Distribution Detection	—Unverified	0
Solving the Real Robot Challenge using Deep Reinforcement Learning	Sep 30, 2021	Deep Reinforcement Learningreinforcement-learning	CodeCode Available	0
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition	Sep 29, 2021	reinforcement-learningReinforcement Learning	—Unverified	0
Simulation-based Bayesian inference for multi-fingered robotic grasping	Sep 29, 2021	Bayesian InferenceRobotic Grasping	—Unverified	0
Sample-Efficient Safety Assurances using Conformal Prediction	Sep 28, 2021	Conformal PredictionPrediction	—Unverified	0
CLIPort: What and Where Pathways for Robotic Manipulation	Sep 24, 2021	Imitation LearningRobotic Grasping	CodeCode Available	1
The Role of Tactile Sensing in Learning and Deploying Grasp Refinement Algorithms	Sep 23, 2021	Reinforcement Learning (RL)Robotic Grasping	CodeCode Available	1
Robust Extrinsic Symmetry Estimation in 3D Point Clouds	Sep 21, 2021	object-detectionObject Detection	—Unverified	0
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation	Sep 19, 2021	Domain AdaptationDomain Generalization	CodeCode Available	1

Show:10 25 50

← PrevPage 15 of 25Next →

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