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 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
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
End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB	Jul 12, 2021	Robotic GraspingSegmentation	CodeCode Available	1
Causal Counterfactuals for Improving the Robustness of Reinforcement Learning	Nov 2, 2022	Causal Inferencereinforcement-learning	CodeCode Available	1
Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks	Nov 22, 2021	Instance SegmentationObject	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
Depth-based 6DoF Object Pose Estimation using Swin Transformer	Mar 3, 2023	6D Pose Estimation6D Pose Estimation using RGB	CodeCode Available	1
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer	Nov 8, 2021	Few-Shot LearningMeta Reinforcement Learning	CodeCode Available	1
Action Conditioned Tactile Prediction: case study on slip prediction	May 19, 2022	PredictionRobotic Grasping	CodeCode Available	1
CLIPort: What and Where Pathways for Robotic Manipulation	Sep 24, 2021	Imitation LearningRobotic 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