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 176–200 of 246 papers

TitleStatusHype
Towards Open-World Grasping with Large Vision-Language Models—0
Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning—0
Towards Precise Robotic Grasping by Probabilistic Post-grasp Displacement Estimation—0
Validate on Sim, Detect on Real -- Model Selection for Domain Randomization—0
ViTaPEs: Visuotactile Position Encodings for Cross-Modal Alignment in Multimodal Transformers—0
VMGNet: A Low Computational Complexity Robotic Grasping Network Based on VMamba with Multi-Scale Feature Fusion—0
WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model—0
When Neural Networks Using Different Sensors Create Similar Features—0
You Only Estimate Once: Unified, One-stage, Real-Time Category-level Articulated Object 6D Pose Estimation for Robotic Grasping—0
You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objects—0
ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping—0
DistillGrasp: Integrating Features Correlation with Knowledge Distillation for Depth Completion of Transparent Objects—0
Open Arms: Open-Source Arms, Hands & Control—0
Perceiving Unseen 3D Objects by Poking the Objects—0
PERCH 2.0 : Fast and Accurate GPU-based Perception via Search for Object Pose Estimation—0
PGA: Personalizing Grasping Agents with Single Human-Robot Interaction—0
PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models—0
Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact—0
Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping—0
Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes—0
PointGuard: Provably Robust 3D Point Cloud Classification—0
Control of the Final-Phase of Closed-Loop Visual Grasping using Image-Based Visual Servoing—0
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping—0
Real-Time Fruit Recognition and Grasping Estimation for Autonomous Apple Harvesting—0
Reinforcement Learning-Based Bionic Reflex Control for Anthropomorphic Robotic Grasping exploiting Domain Randomization—0
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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