21 citations · 41 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…