  When a robot executes a task, it is necessary to model the relationship among
its body, target objects, tools, and environment, and to control its body to
realize the target state. However, it is difficult to model them using
classical methods if the relationship is complex. In addition, when the
relationship changes with time, it is necessary to deal with the temporal
changes of the model. In this study, we have developed Deep Predictive Model
with Parametric Bias (DPMPB) as a more human-like adaptive intelligence to deal
with these modeling difficulties and temporal model changes. We categorize and
summarize the theory of DPMPB and various task experiments on the actual
robots, and discuss the effectiveness of DPMPB.
