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
SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects—0
Speeding up 6-DoF Grasp Sampling with Quality-Diversity—0
Grasping Trajectory Optimization with Point Clouds—0
PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models—0
Jacquard V2: Refining Datasets using the Human In the Loop Data Correction Method—0
Shape-biased Texture Agnostic Representations for Improved Textureless and Metallic Object Detection and 6D Pose EstimationCode0
Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact—0
Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement—0
AGILE: Approach-based Grasp Inference Learned from Element Decomposition—0
Synthetic data enables faster annotation and robust segmentation for multi-object grasping in clutter—0
DynGraspVS: Servoing Aided Grasping for Dynamic EnvironmentsCode0
Reinforcement Learning-Based Bionic Reflex Control for Anthropomorphic Robotic Grasping exploiting Domain Randomization—0
FViT-Grasp: Grasping Objects With Using Fast Vision Transformers—0
PGA: Personalizing Grasping Agents with Single Human-Robot InteractionCode0
Robotic Grasping of Harvested Tomato Trusses Using Vision and Online Learning—0
Robotic Handling of Compliant Food Objects by Robust Learning from Demonstration—0
State Representations as Incentives for Reinforcement Learning Agents: A Sim2Real Analysis on Robotic GraspingCode0
WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Model—0
Instance segmentation based 6D pose estimation of industrial objects using point clouds for robotic bin-picking—0
DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in Cluttered Scenes—0
Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering—0
Self-Supervised Instance Segmentation by Grasping—0
Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural NetworkCode0
Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge—0
Implicit representation priors meet Riemannian geometry for Bayesian 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