64 citations · 64 across the 2 of their papers we have counts for
7 papers · 1 filter
Offline-Online Learning of Deformation Model for Cable Manipulation with Graph Neural Networks
Changhao Wang, Yuyou Zhang, Xiang Zhang +5
Manipulating deformable linear objects by robots has a wide range of applications, e.g., manufacturing and medical surgery. To complete such tasks, an accurate dynamics model for p…
Learn the Manipulation of Deformable Objects Using Tangent Space Point Set Registration
Rui Wang, Te Tang, Masayoshi Tomizuka
Point set registration is a powerful method that enables robots to manipulate deformable objects. By mapping the point cloud of the current object to the pre-trained point cloud, a…
SERoCS: Safe and Efficient Robot Collaborative Systems for Next Generation Intelligent Industrial Co-Robots
Changliu Liu, Te Tang, Hsien-Chung Lin +2
Human-robot collaborations have been recognized as an essential component for future factories. It remains challenging to properly design the behavior of those co-robots. Those rob…
A Learning Framework for Robust Bin Picking by Customized Grippers
Yongxiang Fan, Hsien-Chung Lin, Te Tang +1
Customized grippers have specifically designed fingers to increase the contact area with the workpieces and improve the grasp robustness. However, grasp planning for customized gri…
Real-Time Grasp Planning for Multi-Fingered Hands by Finger Splitting
Yongxiang Fan, Te Tang, Hsien-Chung Lin +1
Grasp planning for multi-fingered hands is computationally expensive due to the joint-contact coupling, surface nonlinearities and high dimensionality, thus is generally not afford…
Grasp Planning for Customized Grippers by Iterative Surface Fitting
Yongxiang Fan, Hsien-Chung Lin, Te Tang +1
Customized grippers have broad applications in industrial assembly lines. Compared with general parallel grippers, the customized grippers have specifically designed fingers to inc…