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 21–30 of 246 papers

Title	Date	Tasks	Status	Hype
Amodal Intra-class Instance Segmentation: Synthetic Datasets and Benchmark	Mar 12, 2023	Amodal Instance SegmentationInstance Segmentation	CodeCode Available	1
Depth-based 6DoF Object Pose Estimation using Swin Transformer	Mar 3, 2023	6D Pose Estimation6D Pose Estimation using RGB	CodeCode Available	1
Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications	Feb 12, 2023	3D Object TrackingObject	CodeCode Available	1
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction	Feb 7, 2023	Instance SegmentationMulti-Object Tracking	CodeCode Available	1
Towards Scale Balanced 6-DoF Grasp Detection in Cluttered Scenes	Dec 10, 2022	Data AugmentationRobotic Grasping	CodeCode Available	1
Causal Counterfactuals for Improving the Robustness of Reinforcement Learning	Nov 2, 2022	Causal Inferencereinforcement-learning	CodeCode Available	1
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions	Sep 15, 2022	DecoderRobotic Grasping	CodeCode Available	1
6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic Grasping	Aug 30, 2022	6D Pose EstimationPose Estimation	CodeCode Available	1
Action Conditioned Tactile Prediction: case study on slip prediction	May 19, 2022	PredictionRobotic Grasping	CodeCode Available	1
6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark	Mar 11, 2022	Pose EstimationRobotic Grasping	CodeCode Available	1

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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