実機ロボットの閉ループ制御改善に向けた情報量の多い軌道による能動的モデル学習
Active Model Learning using Informative Trajectories for Improved Closed-Loop Control on Real Robots
力学モデルの予測不確かさを最小化する情報量の多い軌道をサンプリングベースで探索し、その軌道で実機データを収集してモデルを更新することで、チルトロータ付き全方向飛行ロボットの閉ループ制御性能を向上させた論文。
著者: Weixuan Zhang, Marco Tognon, Lionel Ott, Roland Siegwart, Juan Nieto
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Model-based controllers on real robots require accurate knowledge of the system dynamics to perform optimally. For complex dynamics, first-principles modeling is not sufficiently precise, and data-driven approaches can be leveraged to learn a statistical model from real experiments. However, the efficient and effective data collection for such a data-driven system on real robots is still an open challenge. This paper introduces an optimization problem formulation to find an informative trajectory that allows for efficient data collection and model learning. We present a sampling-based method that computes an approximation of the trajectory that minimizes the prediction uncertainty of the dynamics model. This trajectory is then executed, collecting the data to update the learned model. In experiments we demonstrate the capabilities of our proposed framework when applied to a complex omnidirectional flying vehicle with tiltable rotors. Using our informative trajectories results in models which outperform models obtained from non-informative trajectory by 13.3\% with the same amount of training data. Furthermore, we show that the model learned from informative trajectories generalizes better than the one learned from non-informative trajectories, achieving better tracking performance on different tasks.
