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 121–130 of 246 papers

Title	Date	Tasks	Status
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module	Dec 19, 2018	Face DetectionRobotic Grasping	—Unverified
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition	Sep 29, 2021	reinforcement-learningReinforcement Learning	—Unverified
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition	Feb 10, 2022	reinforcement-learningReinforcement Learning	—Unverified
SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects	Mar 28, 2024	Depth CompletionDepth Estimation	—Unverified
Sample-Efficient Safety Assurances using Conformal Prediction	Sep 28, 2021	Conformal PredictionPrediction	—Unverified
Self-Supervised Instance Segmentation by Grasping	May 10, 2023	Instance SegmentationRobotic Grasping	—Unverified
ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping	Apr 10, 2023	ObjectPose Estimation	—Unverified
Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking	Apr 14, 2022	6D Pose Estimation using RGBPose Estimation	—Unverified
Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks	Dec 18, 2018	Domain AdaptationReinforcement Learning	—Unverified
Simulation-based Bayesian inference for robotic grasping	Mar 10, 2023	Bayesian InferenceRobotic Grasping	—Unverified

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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