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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 246 papers

TitleStatusHype
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
Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU SimulationCode2
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
Free-form language-based robotic reasoning and graspingCode2
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
Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose EstimationCode2
Attribute-Based Robotic Grasping with Data-Efficient Adaptation—0
Prompting Depth Anything for 4K Resolution Accurate Metric Depth EstimationCode5
GaussianProperty: Integrating Physical Properties to 3D Gaussians with LMMs—0
Supertoroid fitting of objects with holes for robotic grasping and scene generationCode0
Measure Anything: Real-time, Multi-stage Vision-based Dimensional Measurement using Segment AnythingCode1
Show:102550
← PrevPage 1 of 10Next →
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