認識的不確かさ下での確率的整合性を保証する集合ベース状態推定
Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty
学習モデルの認識的不確かさを考慮し、ゾノトープで状態を推定して高確率で真の状態を包含することを保証するGP-ZKFを提案し、振り子とロボット介助着衣で有効性を示した。
著者: Shen Li, Theodoros Stouraitis, Michael Gienger, Sethu Vijayakumar, Julie A. Shah
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
Consistent state estimation is challenging, especially under the epistemic uncertainties arising from learned (nonlinear) dynamic and observation models. In this work, we propose a set-based estimation algorithm, named Gaussian Process-Zonotopic Kalman Filter (GP-ZKF), that produces zonotopic state estimates while respecting both the epistemic uncertainties in the learned models and aleatoric uncertainties. Our method guarantees probabilistic consistency, in the sense that the true states are bounded by sets (zonotopes) across all time steps, with high probability. We formally relate GP-ZKF with the corresponding stochastic approach, GP-EKF, in the case of learned (nonlinear) models. In particular, when linearization errors and aleatoric uncertainties are omitted and epistemic uncertainties are simplified, GP-ZKF reduces to GP-EKF. We empirically demonstrate our method's efficacy in both a simulated pendulum domain and a real-world robot-assisted dressing domain, where GP-ZKF produced more consistent and less conservative set-based estimates than all baseline stochastic methods.
