水中ロボットの動力学モデルをオンライン学習するフレームワーク
A Framework for On-line Learning of Underwater Vehicles Dynamic Models
増分サポートベクター回帰とデータの追加・忘却戦略により、環境変化に応じて水中ロボットの動力学モデルをオンラインで適応学習する枠組みを提案し、シミュレーションと実機で有効性を示した。
著者: Bilal Wehbe, Marc Hildebrandt, Frank Kirchner
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Learning the dynamics of robots from data can help achieve more accurate tracking controllers, or aid their navigation algorithms. However, when the actual dynamics of the robots change due to external conditions, on-line adaptation of their models is required to maintain high fidelity performance. In this work, a framework for on-line learning of robot dynamics is developed to adapt to such changes. The proposed framework employs an incremental support vector regression method to learn the model sequentially from data streams. In combination with the incremental learning, strategies for including and forgetting data are developed to obtain better generalization over the whole state space. The framework is tested in simulation and real experimental scenarios demonstrating its adaptation capabilities to changes in the robot's dynamics.
