87 citations · 127 across the 12 of their papers we have counts for
4 papers · 1 filter
Learning to Estimate 3-D States of Deformable Linear Objects from Single-Frame Occluded Point Clouds
Kangchen Lv, Mingrui Yu, Yifan Pu +3
Accurately and robustly estimating the state of deformable linear objects (DLOs), such as ropes and wires, is crucial for DLO manipulation and other applications. However, it remai…
A Coarse-to-Fine Framework for Dual-Arm Manipulation of Deformable Linear Objects with Whole-Body Obstacle Avoidance
Mingrui Yu, Kangchen Lv, Changhao Wang +2
Manipulating deformable linear objects (DLOs) to achieve desired shapes in constrained environments with obstacles is a meaningful but challenging task. Global planning is necessar…
Global Model Learning for Large Deformation Control of Elastic Deformable Linear Objects: An Efficient and Adaptive Approach
Mingrui Yu, Kangchen Lv, Hanzhong Zhong +2
Robotic manipulation of deformable linear objects (DLOs) has broad application prospects in many fields. However, a key issue is to obtain the exact deformation models (i.e., how r…
Glance and Focus Networks for Dynamic Visual Recognition
Gao Huang, Yulin Wang, Kangchen Lv +4
Spatial redundancy widely exists in visual recognition tasks, i.e., discriminative features in an image or video frame usually correspond to only a subset of pixels, while the rema…