21 citations · 41 across the 14 of their papers we have counts for
14 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…
Risk-Aware Wasserstein Distributionally Robust Control of Vessels in Natural Waterways
Juan Moreno Nadales, Astghik Hakobyan, David Muñoz de la Peña +2
In the realm of maritime transportation, autonomous vessel navigation in natural inland waterways faces persistent challenges due to unpredictable natural factors. Existing schedul…
Distributionally Robust Differential Dynamic Programming with Wasserstein Distance
Astghik Hakobyan, Insoon Yang
Differential dynamic programming (DDP) is a popular technique for solving nonlinear optimal control problems with locally quadratic approximations. However, existing DDP methods ar…
Wasserstein Distributionally Robust Control of Partially Observable Linear Stochastic Systems
Astghik Hakobyan, Insoon Yang
Distributionally robust control (DRC) aims to effectively manage distributional ambiguity in stochastic systems. While most existing works address inaccurate distributional informa…
Wasserstein Distributionally Robust Control of Partially Observable Linear Systems: Tractable Approximation and Performance Guarantee
Astghik Hakobyan, Insoon Yang
Wasserstein distributionally robust control (WDRC) is an effective method for addressing inaccurate distribution information about disturbances in stochastic systems. It provides v…
Infusing model predictive control into meta-reinforcement learning for mobile robots in dynamic environments
Jaeuk Shin, Astghik Hakobyan, Mingyu Park +3
The successful operation of mobile robots requires them to adapt rapidly to environmental changes. To develop an adaptive decision-making tool for mobile robots, we propose a novel…
Distributionally robust risk map for learning-based motion planning and control: A semidefinite programming approach
Astghik Hakobyan, Insoon Yang
This paper proposes a novel safety specification tool, called the distributionally robust risk map (DR-risk map), for a mobile robot operating in a learning-enabled environment. Gi…
Learning-based distributionally robust motion control with Gaussian processes
Astghik Hakobyan, Insoon Yang
Safety is a critical issue in learning-based robotic and autonomous systems as learned information about their environments is often unreliable and inaccurate. In this paper, we pr…
Wasserstein Distributionally Robust Motion Control for Collision Avoidance Using Conditional Value-at-Risk
Astghik Hakobyan, Insoon Yang
In this paper, a risk-aware motion control scheme is considered for mobile robots to avoid randomly moving obstacles when the true probability distribution of uncertainty is unknow…