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

TL;DR

This paper formulates a two-level optimization framework integrating control, classical planning, and reinforcement learning to enhance safety, reliability, and interpretability of autonomous systems.

Contribution

It introduces a novel integrated optimization scheme combining control, planning, and learning for autonomous agents, emphasizing safety and interpretability.

Findings

Framework supports safer autonomous operation.

Enhanced reliability through integrated control and planning.

Provides a foundation for future algorithm development.

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.