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 151–175 of 246 papers

TitleStatusHype
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
3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects—0
Adversarial samples for deep monocular 6D object pose estimationCode0
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping BaselineCode0
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition—0
DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Video—0
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images—0
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration—0
MPF6D: Masked Pyramid Fusion 6D Pose Estimation—0
6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping—0
When Neural Networks Using Different Sensors Create Similar Features—0
Validate on Sim, Detect on Real -- Model Selection for Domain Randomization—0
Solving the Real Robot Challenge using Deep Reinforcement LearningCode0
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition—0
Simulation-based Bayesian inference for multi-fingered robotic grasping—0
Sample-Efficient Safety Assurances using Conformal Prediction—0
Robust Extrinsic Symmetry Estimation in 3D Point Clouds—0
ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations—0
Research Challenges and Progress in Robotic Grasping and Manipulation Competitions—0
Domestic waste detection and grasping points for robotic picking up—0
Investigations on Output Parameterizations of Neural Networks for Single Shot 6D Object Pose Estimation—0
Attribute-Based Robotic Grasping with One-Grasp Adaptation—0
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic GraspingCode0
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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