Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations
Summary: arXiv:2507.04356v2 Announce Type: replace-cross
In recent years, the fields of robotics and autonomous systems have experienced unprecedented growth and innovation. As research and investment continue to surge, the prospect of fully autonomous physical agents—ranging from industrial robots to unmanned aerial vehicles—has moved closer to reality. This article explores a novel approach to enhancing the safety and reliability of autonomous systems through a structured methodology that integrates control, classical planning, and reinforcement learning (RL).
Autonomous Physical Agents: An Overview
Autonomous systems are designed to perform tasks with minimal human intervention. This includes a wide array of applications, such as:
- Industrial robots for manufacturing and assembly
- Service robots for hospitality and healthcare
- Unmanned aerial vehicles (UAVs) for surveillance and delivery
- Embedded control devices for home automation
The Need for Enhanced Safety and Reliability
As these systems become more prevalent, ensuring their safety and reliability has become paramount. Traditional methods of operation often fall short in providing adequate interpretability and assurance for users and regulatory bodies. The integration of learning and control systems serves as a crucial step toward addressing these challenges.
Two-Level Reinforcement Learning Approach
This research introduces a stylized version of robotic care that employs a two-level reinforcement learning procedure. This innovative approach trains a policy that encompasses:
- Lower-level physical movement decisions
- Higher-level conceptual tasks and their sub-components
By structuring the learning process into two levels, the system can achieve a higher degree of efficiency and reliability. The integration of control mechanisms at the lower level allows for real-time adjustments to physical movements, while classical planning at the higher level ensures that the overall strategy aligns with mission objectives.
Optimization Framework
The paper presents a general formulation of an optimization scheme that combines the strengths of multiple methodologies. This framework includes:
- Control: Ensures that the physical movements are executed with precision and safety.
- Classical Planning: Provides a structured approach to decision-making and task execution.
- Reinforcement Learning: Facilitates the system’s ability to learn from experience and improve over time.
Conclusion
This research highlights the potential for a synergistic integration of control, planning, and learning methodologies to enhance the performance and reliability of autonomous agents. By addressing the critical aspects of safety and interpretability, this work lays the groundwork for future developments in the field, paving the way for more robust and trustworthy autonomous systems.