[2507.04356] Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations

Mathematics > Optimization and Control arXiv:2507.04356 (math) [Submitted on 6 Jul 2025 (v1), last revised 2 Apr 2026 (this version, v2)] Title:Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations Authors:Vyacheslav Kungurtsev, Monicah Cherop Naibei, Gustav Sir, Akhil Anand, Sebastien Gros, Haozhe Tian, Homayoun Hamedmoghadam View a PDF of the paper titled Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations, by Vyacheslav Kungurtsev and 6 other authors View PDF HTML (experimental) Abstract:Research, innovation and practical capital investment have been increasing rapidly toward the realization of autonomous physical agents. This includes industrial and service robots, unmanned aerial vehicles, embedded control devices, and a number of other realizations of cybernetic/mechatronic implementations of intelligent autonomous devices. In this paper, we consider a stylized version of robotic care, which would normally involve a two-level Reinforcement Learning procedure that trains a policy for both lower level physical movement decisions as well as higher level conceptual tasks and their sub-components. In order to deliver greater safety and reliability in the system, we present the general formulation of this as a two-level optimization scheme which incorporates control at the lower level, and classical planning at the higher level, integrated with a capacity for learning. This synergistic integrat...