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 51–75 of 246 papers

TitleStatusHype
Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive HandsCode1
PyRobot: An Open-source Robotics Framework for Research and BenchmarkingCode1
Real-world multiobject, multigrasp detectionCode1
Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image MatchingCode1
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic GraspingCode1
Rethinking the Inception Architecture for Computer VisionCode1
Real-Time Grasp Detection Using Convolutional Neural NetworksCode1
MTF-Grasp: A Multi-tier Federated Learning Approach for Robotic Grasping—0
Consensus-Driven Uncertainty for Robotic Grasping based on RGB PerceptionCode0
JENGA: Object selection and pose estimation for robotic grasping from a stack—0
You Only Estimate Once: Unified, One-stage, Real-Time Category-level Articulated Object 6D Pose Estimation for Robotic Grasping—0
Category-Level 6D Object Pose Estimation in Agricultural Settings Using a Lattice-Deformation Framework and Diffusion-Augmented Synthetic Data—0
SR3D: Unleashing Single-view 3D Reconstruction for Transparent and Specular Object Grasping—0
Spatial RoboGrasp: Generalized Robotic Grasping Control Policy—0
ViTaPEs: Visuotactile Position Encodings for Cross-Modal Alignment in Multimodal Transformers—0
Grasp the Graph (GtG) 2.0: Ensemble of GNNs for High-Precision Grasp Pose Detection in Clutter—0
Category-Level and Open-Set Object Pose Estimation for Robotics—0
ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping—0
Corner-Grasp: Multi-Action Grasp Detection and Active Gripper Adaptation for Grasping in Cluttered Environments—0
GAA-TSO: Geometry-Aware Assisted Depth Completion for Transparent and Specular Objects—0
Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting—0
A Benchmarking Study of Vision-based Robotic Grasping AlgorithmsCode0
NeuGrasp: Generalizable Neural Surface Reconstruction with Background Priors for Material-Agnostic Object Grasp Detection—0
RoboGrasp: A Universal Grasping Policy for Robust Robotic Control—0
Attribute-Based Robotic Grasping with Data-Efficient Adaptation—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