パラメトリックバイアスを用いた深層予測モデル学習：モデリングの困難さと時間的変化への対処
Deep Predictive Model Learning with Parametric Bias: Handling Modeling Difficulties and Temporal Model Changes
ロボットの身体・対象物・環境の複雑な関係や時間変化に対応するため、パラメトリックバイアスを導入した深層予測モデル（DPMPB）を開発し、実機実験を通じて有効性を示した。
著者: Kento Kawaharazuka, Kei Okada, Masayuki Inaba
分類: cs.RO
原文アブストラクト
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.
