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 126–150 of 246 papers

TitleStatusHype
ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping—0
Natural Language Robot Programming: NLP integrated with autonomous robotic grasping—0
Bimodal SegNet: Instance Segmentation Fusing Events and RGB Frames for Robotic GraspingCode0
Simulation-based Bayesian inference for robotic grasping—0
Perceiving Unseen 3D Objects by Poking the Objects—0
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment—0
Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning—0
Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding—0
Learning to Generate All Feasible Actions—0
NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis—0
3DSGrasp: 3D Shape-Completion for Robotic Grasp—0
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
Towards Confidence-guided Shape Completion for Robotic ApplicationsCode0
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
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
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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