6 papers
WRAP: Wasserstein-Robust Adaptive Plug-in for Robot Localization
Minhyuk Jang, Astghik Hakobyan, Jungjin Lee +2
Robotic localization under changing sensing conditions can suffer from biased errors and miscalibrated covariances. We present WRAP, an adapter-agnostic Wasserstein-robust plug-in…
Distributionally Robust Planning with Adaptive Control
Astghik Hakobyan, Amaras Nazarians, Aditya Gahlawat +2
Safe operation of autonomous systems requires robustness to both model uncertainty and uncertainty in the environment. We propose DRP-AC, a hierarchical framework fo…
Residual-Aware Distributionally Robust EKF: Absorbing Linearization Mismatch via Wasserstein Ambiguity
Minhyuk Jang, Jungjin Lee, Astghik Hakobyan +2
The extended Kalman filter (EKF) is a cornerstone of nonlinear state estimation, yet its performance is fundamentally limited by noise-model mismatch and linearization errors. We d…
Distributionally Robust Kalman Filter
Minhyuk Jang, Astghik Hakobyan, Insoon Yang
We study state estimation for discrete-time linear stochastic systems under distributional ambiguity in the initial state, process noise, and measurement noise. We propose a noise-…
On the Steady-State Distributionally Robust Kalman Filter
Minhyuk Jang, Astghik Hakobyan, Insoon Yang
State estimation in the presence of uncertain or data-driven noise distributions remains a critical challenge in control and robotics. Although the Kalman filter is the most popula…
Wasserstein Distributionally Robust Control and State Estimation for Partially Observable Linear Systems
Minhyuk Jang, Astghik Hakobyan, Insoon Yang
This paper presents a novel Wasserstein distributionally robust control and state estimation algorithm for partially observable linear stochastic systems, where the probability dis…