  A deep learning-based approach can generalize model performance while
reducing feature design costs by learning end-to-end environment recognition
and motion generation. However, the process incurs huge training data
collection costs and time and human resources for trial-and-error when
involving physical contact with robots. Therefore, we propose ``deep predictive
learning,'' a motion learning concept that assumes imperfections in the
predictive model and minimizes the prediction error with the real-world
situation. Deep predictive learning is inspired by the ``free energy principle
and predictive coding theory,'' which explains how living organisms behave to
minimize the prediction error between the real world and the brain. Robots
predict near-future situations based on sensorimotor information and generate
motions that minimize the gap with reality. The robot can flexibly perform
tasks in unlearned situations by adjusting its motion in real-time while
considering the gap between learning and reality. This paper describes the
concept of deep predictive learning, its implementation, and examples of its
application to real robots. The code and document are available at https:
//ogata-lab.github.io/eipl-docs

Robotics