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 81–90 of 246 papers

Title	Date	Tasks	Status	Score
Learning Object Placements For Relational Instructions by Hallucinating Scene Representations	Jan 23, 2020	Auxiliary LearningRobotic Grasping	CodeCode Available	5
Jacquard: A Large Scale Dataset for Robotic Grasp Detection	Mar 30, 2018	DiversityRobotic Grasping	CodeCode Available	5
Improving Robot Dual-System Motor Learning with Intrinsically Motivated Meta-Control and Latent-Space Experience Imagination	Apr 19, 2020	Robotic Grasping	CodeCode Available	5
Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial Settings	Dec 24, 2020	Deep LearningPose Estimation	CodeCode Available	5
Adversarial samples for deep monocular 6D object pose estimation	Mar 1, 2022	6D Pose Estimation6D Pose Estimation using RGB	CodeCode Available	5
DynGraspVS: Servoing Aided Grasping for Dynamic Environments	Dec 22, 2023	Model Predictive ControlRobotic Grasping	CodeCode Available	5
HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language Models	Sep 16, 2024	AttributeDecoder	CodeCode Available	5
Bimodal SegNet: Instance Segmentation Fusing Events and RGB Frames for Robotic Grasping	Mar 20, 2023	DecoderInstance Segmentation	CodeCode Available	5
Action Priors for Large Action Spaces in Robotics	Jan 11, 2021	reinforcement-learningReinforcement Learning (RL)	CodeCode Available	5
IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks	Nov 17, 2019	Industrial Robotsreinforcement-learning	CodeCode Available	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