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 111–120 of 246 papers

Title	Date	Tasks	Status
Collision-Aware Target-Driven Object Grasping in Constrained Environments	Apr 1, 2021	ObjectRobotic Grasping	—Unverified
Jacquard V2: Refining Datasets using the Human In the Loop Data Correction Method	Feb 8, 2024	Robotic Grasping	—Unverified
Fast Graph-Based Object Segmentation for RGB-D Images	May 12, 2016	ObjectRobotic Grasping	—Unverified
Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures	Oct 9, 2019	ObjectReinforcement Learning	—Unverified
Close the Sim2real Gap via Physically-based Structured Light Synthetic Data Simulation	Jul 17, 2024	Dataset GenerationDeep Learning	—Unverified
Latest Datasets and Technologies Presented in the Workshop on Grasping and Manipulation Datasets	Sep 8, 2016	Robotic Grasping	—Unverified
AirCode: Unobtrusive Physical Tags for Digital Fabrication	Jul 18, 2017	ObjectRobotic Grasping	—Unverified
Learning 6-DoF Object Poses to Grasp Category-level Objects by Language Instructions	May 9, 2022	ObjectObject Localization	—Unverified
Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images	Jun 18, 2018	6D Pose Estimation6D Pose Estimation using RGB	—Unverified
Learning to Generate All Feasible Actions	Jan 26, 2023	AllReinforcement Learning (RL)	—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