Deep Predictive Model Learning With Parametric Bias: Handling Modeling Difficulties and Temporal Model Changes
Kento Kawaharazuka, Kei Okada, Masayuki Inaba · IEEE Robotics & Automation Magazine · 2023
When a robot executes a task, it is necessary to model the relationships among its body, target objects, tools, and environment and to control its body to realize the target state. However, it is difficult to model a relationship using classical methods if it 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 the 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 the DPMPB and various task experiments on the actual robots and discuss the effectiveness of the DPMPB.