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 181–190 of 246 papers

Title	Date	Tasks	Status	Hype
RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real	Jun 16, 2020	reinforcement-learningReinforcement Learning	—Unverified	0
Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation	Jun 12, 2020	Continual LearningRobotic Grasping	—Unverified	0
Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map Representation	Jun 9, 2020	Grasp GenerationRobotic Grasping	CodeCode Available	1
GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping	Jun 1, 2020	Pose PredictionRobotic Grasping	CodeCode Available	1
Event-based Robotic Grasping Detection with Neuromorphic Vision Sensor and Event-Stream Dataset	Apr 28, 2020	Robotic Grasping	CodeCode Available	1
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning	Apr 21, 2020	Continual Learningreinforcement-learning	—Unverified	0
Improving Robot Dual-System Motor Learning with Intrinsically Motivated Meta-Control and Latent-Space Experience Imagination	Apr 19, 2020	Robotic Grasping	CodeCode Available	0
Neuromorphic Event-Based Slip Detection and suppression in Robotic Grasping and Manipulation	Apr 15, 2020	Robotic Grasping	—Unverified	0
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control	Apr 13, 2020	Deep Reinforcement Learningreinforcement-learning	—Unverified	0
Real-Time Fruit Recognition and Grasping Estimation for Autonomous Apple Harvesting	Mar 30, 2020	Instance SegmentationRobotic Grasping	—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