In order to adapt to the complex real world, it is essential not only to acquire an optimal behavior policy by machine learning etc., but also to adjust the behavior itself in real time based on the policy of prediction error minimization from the viewpoint of experience which is interaction between the body and the environment. In this talk, I will introduce an overview of deep predictive learning (DPL) proposed by the authors to realize such "embodied intelligence". I will also introduce the examples of our work with several companies using DPL, the latest research results on tool use and flexible object handling, and an overview of our proposal AIREC (AI-driven Robot for Embrace and Care) in the "Moonshot", a large-scale R&D program in Japan.