Safe LLM-Controlled Robots with Formal Guarantees via Reachability Analysis (2503.03911v1)

Abstract: The deployment of LLMs in robotic systems presents unique safety challenges, particularly in unpredictable environments. Although LLMs, leveraging zero-shot learning, enhance human-robot interaction and decision-making capabilities, their inherent probabilistic nature and lack of formal guarantees raise significant concerns for safety-critical applications. Traditional model-based verification approaches often rely on precise system models, which are difficult to obtain for real-world robotic systems and may not be fully trusted due to modeling inaccuracies, unmodeled dynamics, or environmental uncertainties. To address these challenges, this paper introduces a safety assurance framework for LLM-controlled robots based on data-driven reachability analysis, a formal verification technique that ensures all possible system trajectories remain within safe operational limits. Our framework specifically investigates the problem of instructing an LLM to navigate the robot to a specified goal and assesses its ability to generate low-level control actions that successfully guide the robot safely toward that goal. By leveraging historical data to construct reachable sets of states for the robot-LLM system, our approach provides rigorous safety guarantees against unsafe behaviors without relying on explicit analytical models. We validate the framework through experimental case studies in autonomous navigation and task planning, demonstrating its effectiveness in mitigating risks associated with LLM-generated commands. This work advances the integration of formal methods into LLM-based robotics, offering a principled and practical approach to ensuring safety in next-generation autonomous systems.

• The paper presents a safety framework for LLM-controlled robots using data-driven reachability analysis to provide formal guarantees without needing precise system models.
• It introduces a unified safety framework leveraging zero-shot learning for adaptability while integrating rigorous safety checks through data-driven reachable sets.
• Experimental validation demonstrates the framework's ability to mitigate risks in autonomous navigation and task planning, bridging LLM flexibility with formal safety assurance.

Safe LLM-Controlled Robots with Formal Guarantees via Reachability Analysis

The paper presents a sophisticated framework for ensuring safety in robots controlled by LLMs, addressing the intricacies of deploying LLMs in robotic systems operating in uncertain and dynamic environments. The paper specifically tackles the challenges associated with the probabilistic nature of LLMs, which lack traditional model-based formal guarantees, making them less reliable for safety-critical tasks.

Key Contributions

The primary contribution of this work is the introduction of a safety assurance framework for LLM-based robotic control through data-driven reachability analysis. This methodology diverges from traditional model-based approaches by eliminating the need for precise system models, which are often impractical to define accurately in real-world robotic systems due to unmodeled dynamics or environmental uncertainties.

1. Unified Safety Framework: The proposed framework leverages zero-shot learning, enabling LLMs to adapt to new tasks without specific training while simultaneously integrating rigorous safety checks. This adaptability is crucial for practical deployments, where robots encounter continuously evolving scenarios.
2. Data-Driven Reachability Analysis: By utilizing a data-driven approach, the reachability analysis constructs reachable sets of states based on historical data, circumventing the reliance on exact system models. This approach ensures that all possible trajectories remain within the predefined safety boundaries, even amidst modeling inaccuracies or stochastic environmental conditions.
3. Experimental Validation: The framework's efficacy is demonstrated through meticulous case studies involving autonomous navigation and task planning. In these experiments, the reachability analysis successfully mitigates risks associated with LLM-generated commands, thereby ensuring the system remains within safe operational limits.

Implications and Future Directions

The results imply significant advancements in integrating formal methods with LLM-based robotics, offering a principled approach to safety that bridges the gap between LLM flexibility and safety assurance. This aligns well with the industry's need for reliable human-robot interactions, especially in applications requiring complex decision-making and adaptability to unforeseen environments.

The paper also provides a foundation for various applications, from assistive technologies in healthcare to autonomous vehicles, emphasizing the need for robust, safety-certified systems. Future research could explore enhancements to this framework, possibly by incorporating real-time learning mechanisms or exploring synergistic effects with other safety assurance techniques like formal verification or runtime monitoring. Additionally, expanding the framework to handle multi-agent systems and dynamic interactions could further enhance its applicability in collaborative robotic environments.

In conclusion, this paper presents a significant step toward safer deployment of LLM-controlled robotic systems, marrying the adaptability of LLMs with the rigor of formal safety guarantees through innovative reachability analysis. Such advancements are indispensable for the continued evolution and acceptance of autonomous systems in daily life.