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 126–150 of 246 papers

TitleStatusHype
Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking—0
Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks—0
Simulation-based Bayesian inference for robotic grasping—0
Simulation-based Bayesian inference for multi-fingered robotic grasping—0
SparseGrasp: Robotic Grasping via 3D Semantic Gaussian Splatting from Sparse Multi-View RGB Images—0
Spatial RoboGrasp: Generalized Robotic Grasping Control Policy—0
Speeding up 6-DoF Grasp Sampling with Quality-Diversity—0
SR3D: Unleashing Single-view 3D Reconstruction for Transparent and Specular Object Grasping—0
SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping—0
Synthetic data enables faster annotation and robust segmentation for multi-object grasping in clutter—0
Target-Oriented Object Grasping via Multimodal Human Guidance—0
TARGO: Benchmarking Target-driven Object Grasping under Occlusions—0
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control—0
Towards Cross-device and Training-free Robotic Grasping in 3D Open World—0
Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models—0
Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors—0
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
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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