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

2025-07-06

AI Summary

Autonomous systems (e.g., robots, UAVs) face a fundamental trade-off between physical safety and behavioral interpretability in complex tasks. To address this, we propose a task-aligned hierarchical optimization framework that unifies low-level optimal control, high-level symbolic task planning, and hierarchical reinforcement learning—ensuring safe actuation while enhancing interpretability of high-level task intent. Our key contribution is a mathematically grounded, co-optimizing architecture integrating control, planning, and learning, with rigorously defined formal representations and inter-module interaction protocols. Experiments demonstrate substantial improvements in system reliability and decision transparency under dynamic environmental conditions. The framework provides a scalable theoretical foundation and design paradigm for high-assurance autonomous systems.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Novel mobile and WoT systems, and system-of-systems

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.

Problem

• Develops two-level optimization for autonomous robotic control
• Integrates control, planning, and learning for safety
• Enhances reliability and interpretability in autonomous systems

Innovation

• Two-level Reinforcement Learning for autonomous control
• Integrates control, planning, and learning methodologies
• Enhances safety and reliability in autonomous systems