Pin-Chu Yang — accepted AI-conference papers

2018

Put-in-Box Task Generated from Multiple Discrete Tasks by aHumanoid Robot Using Deep Learning

Kei Kase, Kanata Suzuki, Pin-Chu Yang, Hiroki Mori, Tetsuya Ogata

ICRA 2018poster

For robots to have a wide range of applications, they must be able to execute numerous tasks. However, recent studies into robot manipulation using deep neural networks (DNN) have primarily focused on single tasks. Therefore, we investigate a robot manipulation model that uses DNNs and can execute l…

Cited by 41 Source ↗Scholar ↗

2017

Repeatable Folding Task by Humanoid Robot Worker Using Deep Learning

Pin-Chu Yang, Kazuma Sasaki, Kanata Suzuki, Kei Kase, Shigeki Sugano, Tetsuya Ogata

RA-L 2017

We propose a practical state-of-the-art method to develop a machine-learning-based humanoid robot that can work as a production line worker. The proposed approach provides an intuitive way to collect data and exhibits the following characteristics: task performing capability, task reiteration abilit

Cited by 194 Source ↗Scholar ↗