Sheng Yu

Orcid: 0009-0002-0709-1024

Affiliations:
  • School of Automation, Beijing Institute of Technology, Beijing, China


According to our database1, Sheng Yu authored at least 21 papers between 2022 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
An Effective 6DoF Grasp Detection Framework for Human-Robot Interaction With LLMs.
IEEE Trans. Ind. Electron., July, 2026

RGB-Based Category-Level Object Pose Estimation With Multi Pose Maps for Robotic Grasp Detection.
IEEE Trans Autom. Sci. Eng., 2026

GraphGrasp: Lightweight and Efficient Graph-Guided 6-DoF Robotic Grasp Pose Estimation Network.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025
Category-Level 6-D Object Pose Estimation With Learnable Prior Embeddings for Robotic Grasping.
IEEE Trans. Ind. Electron., November, 2025

6-D Object Pose Estimation Based on Point Pair Matching for Robotic Grasp Detection.
IEEE Trans. Neural Networks Learn. Syst., July, 2025

Category-Level 6-D Object Pose Estimation With Shape Deformation for Robotic Grasp Detection.
IEEE Trans. Neural Networks Learn. Syst., January, 2025

TCRNet: Transparent Object Depth Completion With Cascade Refinements.
IEEE Trans Autom. Sci. Eng., 2025

ZSPose: Instance-Level Zero-Shot Object Pose Estimation With Segment Anything Model.
IEEE Trans Autom. Sci. Eng., 2025

Fast and Accurate Category-Level Object Pose Estimation Without Shape Priors for Robotic Grasp Detection.
IEEE Trans Autom. Sci. Eng., 2025

RCGNet: RGB-based Category-Level 6D Object Pose Estimation with Geometric Guidance.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2025

KeyPose: Category-Level 6D Object Pose Estimation with Self-Adaptive Keypoints.
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024
An Efficient Robotic Pushing and Grasping Method in Cluttered Scene.
IEEE Trans. Cybern., September, 2024

A Novel Robotic Pushing and Grasping Method Based on Vision Transformer and Convolution.
IEEE Trans. Neural Networks Learn. Syst., August, 2024

FANet: Fast and Accurate Robotic Grasp Detection Based on Keypoints.
IEEE Trans Autom. Sci. Eng., July, 2024

Robotic Grasp Detection With 6-D Pose Estimation Based on Graph Convolution and Refinement.
IEEE Trans. Syst. Man Cybern. Syst., June, 2024

CatTrack: Single-Stage Category-Level 6D Object Pose Tracking via Convolution and Vision Transformer.
IEEE Trans. Multim., 2024

Synthetic Depth Image-Based Category-Level Object Pose Estimation With Effective Pose Decoupling and Shape Optimization.
IEEE Trans. Instrum. Meas., 2024

CatFormer: Category-Level 6D Object Pose Estimation with Transformer.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
SKGNet: Robotic Grasp Detection With Selective Kernel Convolution.
IEEE Trans Autom. Sci. Eng., October, 2023

EGNet: Efficient Robotic Grasp Detection Network.
IEEE Trans. Ind. Electron., 2023

2022
SE-ResUNet: A Novel Robotic Grasp Detection Method.
IEEE Robotics Autom. Lett., 2022


  Loading...