ロボット制御における深層学習技術の応用例

抄録

  Deep learning has been gaining attention in various fields over this decade. This class of techniques succeeded in many areas, e.g., image recognition and natural language processing. However, deep learning has yet to be fully applied in some areas due to reasons such as a lack of large-scale training datasets. This paper briefly introduces deep learning and some research examples of deep learning in which the author was involved. The first example demonstrated that countless training data were able to be generated automatically by designing a model and a loss function, and the output behaviors could be fine-tuned by adjusting internal feature variables. The second example demonstrated that the generalization ability of a motion-generation model could be remarkably improved by applying neuroscientific findings to the model structure. Finally, this paper introduces force control as an essential concept in robot applications, including laboratory automation.