[2A3] ロボットと実世界-深層学習のロボット応用

[2A3-05] Tool-use Model Considering Selecting Tool by Deep Learning

Namiko Saito¹, Kitae Kim¹, 〇Dai Ba Nguyen¹, Shingo Murata¹, Tetsuya Ogata², Shigeki Sugano¹ (1. Department of Modern Mechanical Engineering, School of Creative Science and Engineering, Waseda University, 2. Department of Intermedia Art and Science, School of Fundamental Science and Engineering, Waseda University)

キーワード：Tool-use, Tool selection, Recognition robotics

We propose a tool-use model that robots choose and use tools to carry out tasks. In these days, research on the tool-use by robots have been done aiming at robots that are useful in daily life. However, conventional research has two problems. (1)experimenters need to label tools or environment. (2)it is impossible to perform a series of operation from tool selection to task execution. In this research, we propose a model which can solve the two problems, we let a robot select a tool, hold it and perform the task, and have a series of experiences. Then, train the sensory-motor data that acquired during the experience and task command with deep learning. At last, to evaluate the model, we confirmed the ability of motion generation in the untrained situation.