activity
20102024
most citedDeep Reinforcement Learning Based Toolpath Generation for Thermal Uniformity in Laser Powder Bed Fusion Process

27 citations · 53 across the 12 of their papers we have counts for

collaborators

7 papers

cs.RO20232 cited

Support Generation for Robot-Assisted 3D Printing with Curved Layers

Tianyu Zhang, Yuming Huang, Piotr Kukulski +3

Robot-assisted 3D printing has drawn a lot of attention by its capability to fabricate curved layers that are optimized according to different objectives. However, the support gene…

cs.RO2022

OpenPneu: Compact platform for pneumatic actuation with multi-channels

Yingjun Tian, Renbo Su, Xilong Wang +3

This paper presents a compact system, OpenPneu, to support the pneumatic actuation for multi-chambers on soft robots. Micro-pumps are employed in the system to generate airflow and…

cs.RO202213 cited

Optimizing out-of-plane stiffness for soft grippers

Renbo Su, Yingjun Tian, Mingwei Du +1

In this paper, we presented a data-driven framework to optimize the out-of-plane stiffness for soft grippers to achieve mechanical properties as hard-to-twist and easy-to-bend. The…

cs.RO2022

Soft Robotic Mannequin: Design and Algorithm for Deformation Control

Yingjun Tian, Guoxin Fang, Justas Petrulis +2

This paper presents a novel soft robotic system for a deformable mannequin that can be employed to physically realize the 3D geometry of different human bodies. The soft membrane o…

cs.CV20202 cited

Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution

Chuhua Xian, Kun Qian, Zitian Zhang +1

Limited by the cost and technology, the resolution of depth map collected by depth camera is often lower than that of its associated RGB camera. Although there have been many resea…

cs.RO2016

Motion Imitation Based on Sparsely Sampled Correspondence

Shuo Jin, Chengkai Dai, Yang Liu +1

Existing techniques for motion imitation often suffer a certain level of latency due to their computational overhead or a large set of correspondence samples to search. To achieve…