SOTAVerified

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 126–150 of 246 papers

TitleStatusHype
3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects—0
Adversarial samples for deep monocular 6D object pose estimationCode0
When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp DetectionCode1
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping BaselineCode0
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition—0
DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Video—0
You Only Demonstrate Once: Category-Level Manipulation from Single Visual DemonstrationCode2
MetaGraspNet_v0: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse SynthesisCode1
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images—0
CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation TasksCode1
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration—0
Depth-aware Object Segmentation and Grasp Detection for Robotic Picking TasksCode1
MPF6D: Masked Pyramid Fusion 6D Pose Estimation—0
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping—0
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task TransferCode1
When Neural Networks Using Different Sensors Create Similar Features—0
Validate on Sim, Detect on Real -- Model Selection for Domain Randomization—0
Solving the Real Robot Challenge using Deep Reinforcement LearningCode0
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition—0
Simulation-based Bayesian inference for multi-fingered robotic grasping—0
Sample-Efficient Safety Assurances using Conformal Prediction—0
CLIPort: What and Where Pathways for Robotic ManipulationCode1
The Role of Tactile Sensing in Learning and Deploying Grasp Refinement AlgorithmsCode1
Robust Extrinsic Symmetry Estimation in 3D Point Clouds—0
CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from SimulationCode1
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All datasetsGraspNet-1BillionCornell Grasp DatasetJacquard dataset

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FlexLoG-CDmAP56.02—Unverified
2GtG2.0mAP53.42—Unverified
3Scale-Balanced-Grasp-CDmAP48.97—Unverified
4graspness-CDmAP48.75—Unverified
5HGGD-CDmAP47.54—Unverified
6HGGDmAP44.24—Unverified
7graspnet-baseline-CDmAP35.45—Unverified
8graspnet-baselinemAP21.41—Unverified
#ModelMetricClaimedVerifiedStatus
1grasp_det_seg_cnn (rgb only, IW split)5 fold cross validation98.2—Unverified
2GR-ConvNet5 fold cross validation97.7—Unverified
3ResNet50 multi-grasp predictor5 fold cross validation96—Unverified
4Multi-Modal Grasp Predictor5 fold cross validation89.21—Unverified
5AlexNet, MultiGrasp5 fold cross validation88—Unverified
6GGCNN5 fold cross validation73—Unverified
7Fast Search5 fold cross validation60.5—Unverified
#ModelMetricClaimedVerifiedStatus
1Efficient-GraspingAccuracy (%)95.6—Unverified
2GR-ConvNetAccuracy (%)94.6—Unverified
3grasp_det_seg_cnn (rgb only)Accuracy (%)92.95—Unverified