ガウス過程を用いた直列弾性アクチュエータの人間-ロボット相互作用のためのデータ駆動型力オブザーバ
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes
直列弾性アクチュエータを備えたロボットにおいて、未知の動力学をガウス過程回帰で学習し、ベイズフィルタリングで相互作用力を推定する手法を提案。人間-外骨格相互作用実験で有効性を示した。
著者: Samuel Tesfazgi, Markus Keßler, Emilio Trigili, Armin Lederer, Sandra Hirche
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Ensuring safety and adapting to the user's behavior are of paramount importance in physical human-robot interaction. Thus, incorporating elastic actuators in the robot's mechanical design has become popular, since it offers intrinsic compliance and additionally provide a coarse estimate for the interaction force by measuring the deformation of the elastic components. While observer-based methods have been shown to improve these estimates, they rely on accurate models of the system, which are challenging to obtain in complex operating environments. In this work, we overcome this issue by learning the unknown dynamics components using Gaussian process (GP) regression. By employing the learned model in a Bayesian filtering framework, we improve the estimation accuracy and additionally obtain an observer that explicitly considers local model uncertainty in the confidence measure of the state estimate. Furthermore, we derive guaranteed estimation error bounds, thus, facilitating the use in safety-critical applications. We demonstrate the effectiveness of the proposed approach experimentally in a human-exoskeleton interaction scenario.
