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 191–200 of 246 papers

Title	Date	Tasks	Status	Hype
Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands	Mar 7, 2020	6D Pose Estimation6D Pose Estimation using RGB	CodeCode Available	1
Acceleration of Actor-Critic Deep Reinforcement Learning for Visual Grasping in Clutter by State Representation Learning Based on Disentanglement of a Raw Input Image	Feb 27, 2020	Deep Reinforcement LearningDisentanglement	—Unverified	0
Learning Object Placements For Relational Instructions by Hallucinating Scene Representations	Jan 23, 2020	Auxiliary LearningRobotic Grasping	CodeCode Available	0
Control of the Final-Phase of Closed-Loop Visual Grasping using Image-Based Visual Servoing	Jan 16, 2020	ObjectRobotic Grasping	—Unverified	0
Robotic Grasp Manipulation Using Evolutionary Computing and Deep Reinforcement Learning	Jan 15, 2020	Deep Reinforcement LearningPose Estimation	—Unverified	0
Reward Engineering for Object Pick and Place Training	Jan 11, 2020	Objectreinforcement-learning	CodeCode Available	0
IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks	Nov 17, 2019	Industrial Robotsreinforcement-learning	CodeCode Available	0
Self-supervised 3D Shape and Viewpoint Estimation from Single Images for Robotics	Oct 17, 2019	3D Object Reconstruction From A Single Image3D Reconstruction	CodeCode Available	0
Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space	Oct 10, 2019	Deep Reinforcement LearningReinforcement Learning	—Unverified	0
Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors	Oct 9, 2019	Contact DetectionRobotic 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