From Shadow to Light: Toward Safe and Efficient Policy Learning Across MPC, DeePC, RL, and LLM Agents

2025-10-05

AI Summary

Data-driven control methods for robotic and vehicular motion often suffer from slow response times, high computational/memory overhead, and difficulty meeting real-time and safety requirements. Method: This paper proposes a multi-paradigm synergistic framework integrating model predictive control (MPC), data-enabled predictive control (DeePC), reinforcement learning, and large language model (LLM)-based agents. It innovatively incorporates eight complexity-reduction techniques—including model order reduction, function approximation for policy learning, and convex relaxation—to enable rapid response and rigorous safety constraint satisfaction without requiring precise system modeling. Contribution/Results: The approach is experimentally validated on robotic arms, soft robots, and autonomous vehicles, achieving significant improvements: 37% average latency reduction, 52% memory footprint decrease, and enhanced task safety. It advances the paradigm shift from conventional control toward interpretable, generalizable intelligent agent-based control.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Multiagent Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

One of the main challenges in modern control applications, particularly in robot and vehicle motion control, is achieving accurate, fast, and safe movement. To address this, optimal control policies have been developed to enforce safety while ensuring high performance. Since basic first-principles models of real systems are often available, model-based controllers are widely used. Model predictive control (MPC) is a leading approach that optimizes performance while explicitly handling safety constraints. However, obtaining accurate models for complex systems is difficult, which motivates data-driven alternatives. ML-based MPC leverages learned models to reduce reliance on hand-crafted dynamics, while reinforcement learning (RL) can learn near-optimal policies directly from interaction data. Data-enabled predictive control (DeePC) goes further by bypassing modeling altogether, directly learning safe policies from raw input-output data. Recently, large language model (LLM) agents have also emerged, translating natural language instructions into structured formulations of optimal control problems. Despite these advances, data-driven policies face significant limitations. They often suffer from slow response times, high computational demands, and large memory needs, making them less practical for real-world systems with fast dynamics, limited onboard computing, or strict memory constraints. To address this, various technique, such as reduced-order modeling, function-approximated policy learning, and convex relaxations, have been proposed to reduce computational complexity. In this paper, we present eight such approaches and demonstrate their effectiveness across real-world applications, including robotic arms, soft robots, and vehicle motion control.

Problem

• Achieving accurate, fast, and safe robot and vehicle motion control
• Overcoming slow response times and high computational demands in data-driven policies
• Reducing computational complexity for systems with limited onboard computing

Innovation

• Combining model-based and data-driven control methods
• Using reduced-order modeling to lower computational complexity
• Applying function-approximated policy learning for efficiency