← Search

Guanghui Shen

2 accepted papers

2025

Self-Supervised Learning of Reconstructing Deformable Linear Objects Under Single-Frame Occluded View

ICRA 2025

Deformable linear objects (DLOs), such as ropes, cables, and rods, are common in various scenarios, and accurate occlusion reconstruction of them is crucial for effective robotic manipulation. Previous studies for DLO reconstruction either rely on supervised learning, which is limited by the availab

Cited by 1SourceScholar
2025

Visual-Privileged Co-Learning for Industrial Board-to-Board Connectors Force-Guided Assembly Task

RA-L 2025

Automatic assembly of board-to-board (BTB) connectors remains a significant challenge in smartphone manufacturing due to severe visual occlusion, tight assembly tolerances, and process constraints that prohibit separate visual adjustment stations. This letter proposes Visual-Privileged Co-Learning (

Cited by 2SourceScholar