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
Domestic waste detection and grasping points for robotic picking up—0
Edge-Based Recognition of Novel Objects for Robotic Grasping—0
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning—0
Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation—0
Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation—0
Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space—0
End-to-End Learning of Semantic Grasping—0
Evaluating Gaussian Grasp Maps for Generative Grasping Models—0
Fast Graph-Based Object Segmentation for RGB-D Images—0
Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge—0
FastOrient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping—0
From Words to Poses: Enhancing Novel Object Pose Estimation with Vision Language Models—0
FViT-Grasp: Grasping Objects With Using Fast Vision Transformers—0
GAA-TSO: Geometry-Aware Assisted Depth Completion for Transparent and Specular Objects—0
GaussianProperty: Integrating Physical Properties to 3D Gaussians with LMMs—0
GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping—0
GP-net: Flexible Viewpoint Grasp Proposal—0
Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent—0
Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting—0
Grasping Trajectory Optimization with Point Clouds—0
Robots Enact Malignant Stereotypes—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
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
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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