26 citations · 54 across the 4 of their papers we have counts for
4 papers
Deep Reinforcement Learning Based on Local GNN for Goal-conditioned Deformable Object Rearranging
Yuhong Deng, Chongkun Xia, Xueqian Wang +1
Object rearranging is one of the most common deformable manipulation tasks, where the robot needs to rearrange a deformable object into a goal configuration. Previous studies focus…
Graph-Transporter: A Graph-based Learning Method for Goal-Conditioned Deformable Object Rearranging Task
Yuhong Deng, Chongkun Xia, Xueqian Wang +1
Rearranging deformable objects is a long-standing challenge in robotic manipulation for the high dimensionality of configuration space and the complex dynamics of deformable object…
Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention
Kai Mo, Chongkun Xia, Xueqian Wang +3
Sequential multi-step cloth manipulation is a challenging problem in robotic manipulation, requiring a robot to perceive the cloth state and plan a sequence of chained actions lead…
Polarimetric Inverse Rendering for Transparent Shapes Reconstruction
Mingqi Shao, Chongkun Xia, Dongxu Duan +1
In this work, we propose a novel method for the detailed reconstruction of transparent objects by exploiting polarimetric cues. Most of the existing methods usually lack sufficient…