Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty
This work addresses consistent state estimation for robotics under epistemic uncertainty, representing an incremental improvement with a novel hybrid method.
The paper tackled state estimation under epistemic uncertainty from learned nonlinear models by proposing the GP-ZKF algorithm, which guarantees probabilistic consistency with zonotopic bounds and outperformed stochastic baselines in simulated and real-world robot-assisted dressing tasks.
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.