90 citations · 160 across the 10 of their papers we have counts for
6 papers · 1 filter
Learning visual-based deformable object rearrangement with local graph neural networks
Yuhong Deng, Xueqian Wang, Lipeng chen
Goal-conditioned rearrangement of deformable objects (e.g. straightening a rope and folding a cloth) is one of the most common deformable manipulation tasks, where the robot needs…
Learning Language-Conditioned Deformable Object Manipulation with Graph Dynamics
Yuhong Deng, Kai Mo, Chongkun Xia +1
Multi-task learning of deformable object manipulation is a challenging problem in robot manipulation. Most previous works address this problem in a goal-conditioned way and adapt g…
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment
Yuhong Deng, Xiaofeng Guo, Yixuan Wei +5
In this paper, a novel robotic grasping system is established to automatically pick up objects in cluttered scenes. A composite robotic hand composed of a suction cup and a gripper…
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…