  Bridging the gap between motion models and reality is crucial by using
limited data to deploy robots in the real world. Deep learning is expected to
be generalized to diverse situations while reducing feature design costs
through end-to-end learning for environmental recognition and motion
generation. However, data collection for model training is costly, and time and
human resources are essential for robot trial-and-error with physical contact.
We propose "Deep Predictive Learning," a motion learning concept that predicts
the robot's sensorimotor dynamics, assuming imperfections in the prediction
model. The predictive coding theory inspires this concept to solve the above
problems. It is based on the fundamental strategy of predicting the near-future
sensorimotor states of robots and online minimization of the prediction error
between the real world and the model. Based on the acquired sensor information,
the robot can adjust its behavior in real time, thereby tolerating the
difference between the learning experience and reality. Additionally, the robot
was expected to perform a wide range of tasks by combining the motion dynamics
embedded in the model. This paper describes the proposed concept, its
implementation, and examples of its applications in real robots. The code and
documents are available at: https://ogata-lab.github.io/eipl-docs
