activity
20172022
most citedOffline-Online Learning of Deformation Model for Cable Manipulation with Graph Neural Networks

64 citations · 84 across the 7 of their papers we have counts for

collaborators
Showing 2018Show all

5 papers · 1 filter

cs.RO2018

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…

cs.RO2018★ 15 cited

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…

cs.RO2018★ 2 cited

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…

cs.RO2018

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…

cs.RO2018★ 3 cited

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…