13 citations · 33 across the 15 of their papers we have counts for
3 papers · 1 filter
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…