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.

翻译：当机器人执行任务时，需要对其身体、目标物体、工具与环境之间的关系进行建模，并通过控制身体来实现目标状态。然而，若关系复杂，使用经典方法建模将十分困难。此外，当关系随时间变化时，还需应对模型的时间动态变化。在本研究中，我们开发了基于参数偏差的深度预测模型（DPMPB），作为一种更接近人类的自适应智能体，以处理这些建模困难与时间模型变化。本文对DPMPB理论及其在实际机器人上的多种任务实验进行了分类总结，并探讨了DPMPB的有效性。