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

翻译：基于深度学习的方法可以通过端到端的环境识别与运动生成来提升模型泛化能力，同时降低特征设计成本。然而，当涉及机器人与物理世界的接触时，该方法会带来巨大的训练数据收集成本，以及反复试错所需的时间和人力资源投入。为此，我们提出“深度预测学习”——一种假设预测模型存在不完美性、并致力于最小化现实情境下预测误差的运动学习概念。深度预测学习受“自由能原理与预测编码理论”启发，该理论阐释了生物体如何通过行为最小化现实世界与大脑之间的预测误差。机器人基于感觉运动信息预测近期情境，并通过生成运动来缩小与现实的差距。通过实时调整运动并考虑学习与现实之间的差异，机器人可在未学习情境中灵活执行任务。本文阐述了深度预测学习的概念、实现方法及其在真实机器人上的应用实例。相关代码与文档可访问 https://ogata-lab.github.io/eipl-docs 获取。