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

arXiv cs.RO / 4/6/2026

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Key Points

The paper proposes a two-level optimization framework for autonomous physical systems that combines lower-level control, higher-level classical planning, and learning components for improved safety and reliability.
It frames the learning problem as a stylized robotic-care task where a single two-level procedure would train both physical movement policies and higher-level conceptual task decisions.
Reliability is explicitly defined to include physical safety as well as interpretability, reducing concerns about “black box” behavior for users and regulators.
The work provides the foundational formulation and integration details for the combined control–planning–RL approach, aiming to guide future algorithm development toward more efficient performance.
By unifying multiple methodologies, the authors argue the framework can yield better insight into how to design algorithms that meet practical autonomy constraints.

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 integration of multiple methodologies -- control, classical planning, and RL -- presents an opportunity for greater insight for algorithm development, leading to more efficient and reliable performance. Here, the notion of reliability pertains to physical safety and interpretability into an otherwise black box operation of autonomous agents, concerning users and regulators. This work presents the necessary background and general formulation of the optimization framework, detailing each component and its integration with the others.