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 231–240 of 246 papers

Title	Date	Tasks	Status
Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network	May 5, 2023	Panoptic SegmentationRobotic Grasping	CodeCode Available
Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial Settings	Dec 24, 2020	Deep LearningPose Estimation	CodeCode Available
DynGraspVS: Servoing Aided Grasping for Dynamic Environments	Dec 22, 2023	Model Predictive ControlRobotic Grasping	CodeCode Available
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods	Feb 28, 2018	Deep Reinforcement LearningDiversity	CodeCode Available
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
State Representations as Incentives for Reinforcement Learning Agents: A Sim2Real Analysis on Robotic Grasping	Sep 21, 2023	Decision Makingreinforcement-learning	CodeCode Available
Towards Real-World Efficiency: Domain Randomization in Reinforcement Learning for Pre-Capture of Free-Floating Moving Targets by Autonomous Robots	Jun 10, 2024	Deep Reinforcement LearningNavigate	CodeCode Available
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping	Apr 2, 2021	Contrastive LearningDecoder	CodeCode Available
Reward Engineering for Object Pick and Place Training	Jan 11, 2020	Objectreinforcement-learning	CodeCode Available
Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network	Sep 11, 2019	Robotic Grasping	CodeCode Available

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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