Abstract
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.
Abstract (translated)
当机器人执行任务时，需要建模其身体、目标物体、工具和环境之间的关系，并控制其身体以实现目标状态。然而，使用经典方法建模这些关系会很难。此外，当关系随时间发生变化时，需要处理模型的时间变化。在本研究中，我们开发了具有参数偏差的深度预测模型（DPMPB）作为更人性化的自适应智能来处理这些建模困难以及随时间变化的模型。我们对DPMPB的理论进行了分类和总结，并讨论了DPMPB的有效性。
URL
https://arxiv.org/abs/2404.15726
