パラメトリックバイアス付き確率的予測ネットワークによる分散最小化を含む環境適応制御：移動ロボットへの応用
Environmentally Adaptive Control Including Variance Minimization Using Stochastic Predictive Network with Parametric Bias: Application to Mobile Robots
パラメトリックバイアスと確率的要素を含むリカレントニューラルネットワークで予測モデルを構築し、分散を最小化しながら環境に適応するロボット制御法を提案。シミュレーションと実機Fetchで検証した。
著者: Kento Kawaharazuka, Koki Shinjo, Yoichiro Kawamura, Kei Okada, Masayuki Inaba
分類: cs.RO
原文アブストラクト
In this study, we propose a predictive model composed of a recurrent neural network including parametric bias and stochastic elements, and an environmentally adaptive robot control method including variance minimization using the model. Robots which have flexible bodies or whose states can only be partially observed are difficult to modelize, and their predictive models often have stochastic behaviors. In addition, the physical state of the robot and the surrounding environment change sequentially, and so the predictive model can change online. Therefore, in this study, we construct a learning-based stochastic predictive model implemented in a neural network embedded with such information from the experience of the robot, and develop a control method for the robot to avoid unstable motion with large variance while adapting to the current environment. This method is verified through a mobile robot in simulation and to the actual robot Fetch.
