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 211–220 of 246 papers

Title	Date	Tasks	Status
Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network	Sep 11, 2019	Robotic Grasping	CodeCode Available
Towards Precise Robotic Grasping by Probabilistic Post-grasp Displacement Estimation	Sep 4, 2019	ObjectRobotic Grasping	—Unverified
Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks	Jun 21, 2019	3D Shape RepresentationObject	—Unverified
Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A Review	May 16, 2019	6D Pose Estimation6D Pose Estimation using RGB	CodeCode Available
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping	Apr 15, 2019	Robotic Grasping	—Unverified
Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes	Mar 8, 2019	reinforcement-learningReinforcement Learning	—Unverified
The RobotriX: An eXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and Interactions	Jan 19, 2019	Robotic Grasping	CodeCode Available
3D Convolution on RGB-D Point Clouds for Accurate Model-free Object Pose Estimation	Dec 29, 2018	Pose EstimationRobotic Grasping	—Unverified
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module	Dec 19, 2018	Face DetectionRobotic Grasping	—Unverified
Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks	Dec 18, 2018	Domain AdaptationReinforcement Learning	—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