Effective Imitation Learning Robot Platform using Game Engine
Pin-Chu Yang, Kanata Suzuki, Chang-Chieh Chiu, Tito Pradhono Tomo, Nelson Yalta, Kevin KUO, Kuo-Hao Shu, Tetsuya Ogata · The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2020
This study proposed an effective imitation learning humanoid robot platform based on a Game Engine which considered usual creators of 3DCG animator or game creator’s usual development environment. We verify the proposed platform with an actual imitation learning task which is trained our robot to learn to generate 10 different action patterns. Each action pattern contains time-series motor angle information, facial animation command and voice command. Finally, we evaluate the man-hour cost through the instructor of the Japanese Industrial Standards(JIS Z 8141-1227) and show a 60% reduction of time cost for executing the same manner to a similar setup.