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 101–125 of 246 papers

TitleStatusHype
NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis—0
3DSGrasp: 3D Shape-Completion for Robotic Grasp—0
Towards Scale Balanced 6-DoF Grasp Detection in Cluttered ScenesCode1
Causal Counterfactuals for Improving the Robustness of Reinforcement LearningCode1
One-Shot Neural Fields for 3D Object Understanding—0
Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint—0
MonoGraspNet: 6-DoF Grasping with a Single RGB Image—0
GP-net: Flexible Viewpoint Grasp Proposal—0
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-ResolutionsCode1
Towards Confidence-guided Shape Completion for Robotic ApplicationsCode0
6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic GraspingCode1
MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object DetectionCode0
Learning to Grasp on the Moon from 3D Octree Observations with Deep Reinforcement LearningCode0
Robots Enact Malignant Stereotypes—0
Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation—0
Evaluating Gaussian Grasp Maps for Generative Grasping Models—0
Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping—0
Open Arms: Open-Source Arms, Hands & Control—0
Action Conditioned Tactile Prediction: case study on slip predictionCode1
Learning 6-DoF Object Poses to Grasp Category-level Objects by Language Instructions—0
HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation—0
GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping—0
Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking—0
Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints—0
6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and BenchmarkCode1
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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