LC-SAC: Lyapunov-Constrained Soft Actor-Critic via Koopman Operator Theory for Trajectory Tracking and Stabilization
arXiv:2602.04132v4 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) has achieved remarkable success in solving complex sequential decision-making problems. However, its application to safety-critical physical systems remains constrained by the lack of stability guarantees. Standard RL algorithms prioritize reward maximization, often yielding policies that may induce oscillations or unbounded state divergence. In this work we propose a Lyapunov-Constrained Soft Actor-Critic (LC-SAC) algorithm using Koopman operator theory. We learn a linear lifted surrogate of the error dynami
The increasing deployment of AI in physical systems demands robust safety guarantees, a critical missing piece in current generative AI paradigms for real-world applications.
This research addresses a fundamental limitation of AI in safety-critical applications, paving the way for more widespread and trustworthy integration of AI into physical infrastructure and machinery.
AI systems can now be developed with inherent stability guarantees, reducing risks of unpredictable behavior in sensitive applications like industrial automation, robotics, and autonomous systems.
- · Robotics manufacturers
- · Industrial automation sector
- · Autonomous vehicle developers
- · AI safety researchers
- · Companies relying on unreliable AI for safety-critical tasks
- · Traditional control system engineers without AI integration skills
Enhances the safety and predictability of AI deployed in physical systems, fostering greater trust and adoption.
Accelerates the development and commercialization of advanced robotics and autonomous intelligent agents in complex environments.
Potentially enables entirely new classes of intelligent infrastructure and manufacturing processes with embedded, guaranteed safe AI.
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Read at arXiv cs.LG