Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty

TL;DR

This paper introduces GP-ZKF, a set-based state estimation method that guarantees probabilistic consistency under epistemic and aleatoric uncertainties, demonstrated to outperform stochastic methods in simulations and real-world applications.

Contribution

The paper presents a novel set-based Kalman filter that accounts for epistemic uncertainties and guarantees probabilistic consistency, bridging the gap with stochastic approaches.

Findings

GP-ZKF produces more consistent estimates than baseline methods.

The method guarantees probabilistic bounds on true states.

Empirical results show effectiveness in both simulation and real-world scenarios.

Abstract

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.