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 51–60 of 246 papers

Title	Date	Tasks	Status	Hype	Score
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions	Sep 15, 2022	DecoderRobotic Grasping	CodeCode Available	1	5
Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agents	Apr 12, 2024	Language ModellingLarge Language Model	CodeCode Available	1	5
6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic Grasping	Aug 30, 2022	6D Pose EstimationPose Estimation	CodeCode Available	1	5
Real-Time Grasp Detection Using Convolutional Neural Networks	Dec 9, 2014	General ClassificationGPU	CodeCode Available	1	5
Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands	Mar 7, 2020	6D Pose Estimation6D Pose Estimation using RGB	CodeCode Available	1	5
Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks	Nov 22, 2021	Instance SegmentationObject	CodeCode Available	1	5
Depth-based 6DoF Object Pose Estimation using Swin Transformer	Mar 3, 2023	6D Pose Estimation6D Pose Estimation using RGB	CodeCode Available	1	5
MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object Detection	Aug 19, 2022	3D Object DetectionAutonomous Driving	CodeCode Available	0	5
Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in Clutter	Sep 24, 2019	Data AugmentationImitation Learning	CodeCode Available	0	5
Learning Object Placements For Relational Instructions by Hallucinating Scene Representations	Jan 23, 2020	Auxiliary LearningRobotic Grasping	CodeCode Available	0	5

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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