13 citations · 33 across the 15 of their papers we have counts for
16 papers
Meta-Learning for Rapid Adaptation in Reference Tracking of Uncertain Nonlinear Systems
Jiaqi Yan, Ankush Chakrabarty, Niklas Schmid +2
In this paper, we address the problem of reference tracking for uncertain nonlinear systems. Since collecting data from the target system (i.e., the system of interest) is often ch…
Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning
Sleiman Safaoui, Abraham P. Vinod, Ankush Chakrabarty +3
We consider the problem of safe multi-agent motion planning for drones in uncertain, cluttered workspaces. For this problem, we present a tractable motion planner that builds upon…
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Truong X. Nghiem, Ján Drgoňa, Colin Jones +10
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathema…
Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints
Wenjie Xu, Colin N Jones, Bratislav Svetozarevic +2
We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for improv…
Meta-Learning of Neural State-Space Models Using Data From Similar Systems
Ankush Chakrabarty, Gordon Wichern, Christopher R. Laughman
Deep neural state-space models (SSMs) provide a powerful tool for modeling dynamical systems solely using operational data. Typically, neural SSMs are trained using data collected…
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach
Ankush Chakrabarty
Bayesian optimization (BO) has demonstrated potential for optimizing control performance in data-limited settings, especially for systems with unknown dynamics or unmodeled perform…