深層学習を用いた道具と物体の関係性を考慮した道具使用モデル

抄録

We propose a tool-use model which considers relationship between tools and target objects. Robots with tool-use ability are useful in human living space. However, in previous research, robots could not manipulate arbitrary objects with arbitrary tools without requiring any human assistance. In this study, we construct a model that makes robots consider the relationship between tools and objects by themselves and realize robots’ object manipulation with tools. For this purpose, we let a robot experience some tool-use tasks and train sensory-motor data recorded during the experience with deep learning. To let robots consider the relationship, it is necessary to set up the tasks, that include information of four factors: (1) tools, (2) objects, (3) actions and (4) effect. In model evaluation, we analyze whether the robot can detect the relationship between unknown tools and objects.