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 61–70 of 246 papers

Title	Date	Tasks	Status	Score
MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object Detection	Aug 19, 2022	3D Object DetectionAutonomous Driving	CodeCode Available	5
Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network	May 5, 2023	Panoptic SegmentationRobotic Grasping	CodeCode Available	5
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods	Feb 28, 2018	Deep Reinforcement LearningDiversity	CodeCode Available	5
Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network	Sep 11, 2019	Robotic Grasping	CodeCode Available	5
Learning to Grasp on the Moon from 3D Octree Observations with Deep Reinforcement Learning	Aug 1, 2022	Deep Reinforcement LearningReinforcement Learning (RL)	CodeCode Available	5
3D Semantic Segmentation of Modular Furniture using rjMCMC	May 15, 2017	3D Semantic Segmentationfurniture segmentation	CodeCode Available	5
Learning Object Placements For Relational Instructions by Hallucinating Scene Representations	Jan 23, 2020	Auxiliary LearningRobotic Grasping	CodeCode Available	5
Improving Robot Dual-System Motor Learning with Intrinsically Motivated Meta-Control and Latent-Space Experience Imagination	Apr 19, 2020	Robotic Grasping	CodeCode Available	5
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping	Apr 2, 2021	Contrastive LearningDecoder	CodeCode Available	5
A Benchmarking Study of Vision-based Robotic Grasping Algorithms	Mar 14, 2025	BenchmarkingRobotic Grasping	CodeCode Available	5

Show:10 25 50

← PrevPage 7 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