Stochastic Online Optimization for Cyber-Physical and Robotic Systems

arXiv:2404.05318v1 Announce Type: new
Abstract: We propose a novel gradient-based online optimization framework for solving stochastic programming problems that frequently arise in the context of cyber-physical and robotic systems. Our problem formulation accommodates constraints that model the evolution of a cyber-physical system, which has, in general, a continuous state and action space, is nonlinear, and where the state is only partially observed. We also incorporate an approximate model of the dynamics as prior knowledge into the learning process and show …

abstract arxiv constraints context continuous cs.lg cs.ro cyber evolution framework general gradient novel optimization programming robotic space state stochastic systems type