Real-to-Sim: 学習ベースUKFによる疎データからのロボット残差誤差予測
Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter
ニューラルネットワークのパラメータをUnscented Kalman Filterで更新し、少ないデータから動力学モデルやシミュレータと実機の残差誤差を学習してリアルギャップを縮める手法を提案。
著者: Alexander Schperberg, Yusuke Tanaka, Feng Xu, Marcel Menner, Dennis Hong
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Achieving highly accurate dynamic or simulator models that are close to the real robot can facilitate model-based controls (e.g., model predictive control or linear-quadradic regulators), model-based trajectory planning (e.g., trajectory optimization), and decrease the amount of learning time necessary for reinforcement learning methods. Thus, the objective of this work is to learn the residual errors between a dynamic and/or simulator model and the real robot. This is achieved using a neural network, where the parameters of a neural network are updated through an Unscented Kalman Filter (UKF) formulation. Using this method, we model these residual errors with only small amounts of data -- a necessity as we improve the simulator/dynamic model by learning directly from real-world operation. We demonstrate our method on robotic hardware (e.g., manipulator arm, and a wheeled robot), and show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
