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 151–175 of 246 papers

TitleStatusHype
Grasping Trajectory Optimization with Point Clouds—0
Robust Extrinsic Symmetry Estimation in 3D Point Clouds—0
Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Network with Rotation Ensemble Module—0
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition—0
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition—0
SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects—0
Sample-Efficient Safety Assurances using Conformal Prediction—0
Self-Supervised Instance Segmentation by Grasping—0
ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping—0
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
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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