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

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 expert defined optimization scheme which incorporates control with physics modelling of the system and environment at the lower level, and classical planning with complex task logic 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 principled integration of the two expert‐defined domains. The methodology is validated in a simulation of robotic care aid, with complex medication and meal preparation tasks at the upper level and locomotion at the lower level. The validation confirms the framework's adaptability to human preferences and symbolic to continuous feedback.