リーマン多様体上のMatérnカーネルを用いた幾何学考慮型ベイズ最適化
Geometry-aware Bayesian Optimization in Robotics using Riemannian Mat\'ern Kernels
球面や回転群など非ユークリッド空間上の関数を対象に、リーマンMatérnカーネルを実装してベイズ最適化を行い、姿勢制御や可操作度最適化、動作計画で性能向上を示した論文。
著者: Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky, Alexander Terenin, Tamim Asfour, Leonel Rozo
分類: cs.RO, cs.LG
原文アブストラクト
Bayesian optimization is a data-efficient technique which can be used for control parameter tuning, parametric policy adaptation, and structure design in robotics. Many of these problems require optimization of functions defined on non-Euclidean domains like spheres, rotation groups, or spaces of positive-definite matrices. To do so, one must place a Gaussian process prior, or equivalently define a kernel, on the space of interest. Effective kernels typically reflect the geometry of the spaces they are defined on, but designing them is generally non-trivial. Recent work on the Riemannian Mat\'ern kernels, based on stochastic partial differential equations and spectral theory of the Laplace-Beltrami operator, offers promising avenues towards constructing such geometry-aware kernels. In this paper, we study techniques for implementing these kernels on manifolds of interest in robotics, demonstrate their performance on a set of artificial benchmark functions, and illustrate geometry-aware Bayesian optimization for a variety of robotic applications, covering orientation control, manipulability optimization, and motion planning, while showing its improved performance.
