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	Score
Causal Counterfactuals for Improving the Robustness of Reinforcement Learning	Nov 2, 2022	Causal Inferencereinforcement-learning	CodeCode Available	1	5
CLIPort: What and Where Pathways for Robotic Manipulation	Sep 24, 2021	Imitation LearningRobotic Grasping	CodeCode Available	1	5
Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks	Nov 22, 2021	Instance SegmentationObject	CodeCode Available	1	5
Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasets	Oct 6, 2023	DiversityRobotic Grasping	CodeCode Available	1	5
CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks	Dec 6, 2021	Continuous ControlImitation Learning	CodeCode Available	1	5
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer	Nov 8, 2021	Few-Shot LearningMeta Reinforcement Learning	CodeCode Available	1	5
Learning Dexterous Grasping with Object-Centric Visual Affordances	Sep 3, 2020	Deep Reinforcement LearningObject	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
6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark	Mar 11, 2022	Pose EstimationRobotic Grasping	CodeCode Available	1	5
End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB	Jul 12, 2021	Robotic GraspingSegmentation	CodeCode Available	1	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