[2M5-OS-19c] 世界モデルと知能(3/4)

[2M5-OS-19c-01] Deep Predictive Model Learning with Parametric Bias and Its Application to Various Robots

〇Kento Kawaharazuka¹, Kei Okada¹, Masayuki Inaba¹ (1. The University of Tokyo)

[[Online]]

Keywords:Robotics, Deep Learning, Predictive Model

When a robot performs a task, it is necessary to modelize the relationships among its body, target objects, tools, and environments, and to control the body so as to realize the target states. However, when these relationships are complex, it is difficult to modelize them using classical methods, and when these relationships change with time, it is necessary to deal with the temporal changes in the model. In this study, we have developed Deep Predictive Model with Parametric Bias (DPMPB) to cope with this modeling difficulties and temporal model changes. We summarize the theory and experiments on various robots, and discuss its effectiveness.