パラメータ不確かさ下でのモデルベース汎化のための経路積分制御
Model-Based Generalization Under Parameter Uncertainty Using Path Integral Control
経路積分制御の自由エネルギー定式化でサンプル空間を拡張し、モデルの不確かさを行動に埋め込むことでロボット計画の頑健性を高める手法を提案し、多様なロボットタスクで実機検証した。
著者: Ian Abraham, Ankur Handa, Nathan Ratliff, Kendall Lowrey, Todd D. Murphey, Dieter Fox
分類: cs.RO
原文アブストラクト
This work addresses the problem of robot interaction in complex environments where online control and adaptation is necessary. By expanding the sample space in the free energy formulation of path integral control, we derive a natural extension to the path integral control that embeds uncertainty into action and provides robustness for model-based robot planning. Our algorithm is applied to a diverse set of tasks using different robots and validate our results in simulation and real-world experiments. We further show that our method is capable of running in real-time without loss of performance. Videos of the experiments as well as additional implementation details can be found at https://sites.google.com/view/emppi.
