INPROVF: Leveraging Large Language Models to Repair High-level Robot Controllers from Assumption Violations
INPROVF integrates LLMs and formal verification to efficiently repair high-level robot controllers during assumption violations, reducing repair time by up to 70%.
Key Findings
Methodology
INPROVF employs a hybrid approach combining symbolic abstraction-to-natural language translation, leveraging LLMs (e.g., GPT-4) to generate repair skill candidates, and formal verification via GR(1) synthesis tools like Slugs to validate their correctness. The framework includes four core steps: informalization of environment and strategy descriptions, candidate generation through LLM prompts, syntax and realizability verification, and iterative feedback based on counterexamples. This integration allows scalable repair in large state spaces by reducing computational complexity and enabling online, iterative refinement. The approach ensures high accuracy (>95%) in candidate validation and significantly accelerates repair times compared to traditional formal methods.
Key Results
- Across 12 diverse scenarios involving workspace sizes from hundreds to thousands of states, INPROVF achieved an average repair time reduction of 70% relative to baseline formal methods, with success rates exceeding 90%. The system effectively generated multiple repair candidates, with a validation accuracy above 98%, demonstrating robustness. Experimental results showed that the method maintained high success rates even in complex tasks like manipulation, navigation, and obstacle avoidance, confirming its scalability and adaptability.
- Compared to pure formal synthesis, INPROVF reduced repair durations by a factor of 2-3, especially in high-dimensional spaces. The iterative feedback mechanism, based on counterexamples, improved candidate quality and reduced trial iterations. The use of GPT-4 for candidate generation provided diverse solutions, which were then rigorously verified, ensuring both efficiency and correctness. The results validate the approach's potential for real-time autonomous repair in complex environments.
- The experiments confirmed that combining natural language descriptions with formal verification creates a scalable, reliable repair pipeline. The feedback loop effectively filters out invalid candidates, leading to faster convergence. The approach demonstrated strong generalization across different tasks and environment complexities, highlighting its broad applicability. Overall, INPROVF offers a practical solution for autonomous systems requiring rapid, safe recovery from assumption violations.
Significance
This work addresses a fundamental challenge in autonomous robotics: maintaining safety and task completion amid environment assumption violations, especially in large, complex state spaces. By integrating large language models with formal verification, INPROVF significantly reduces computational costs and enhances scalability, enabling real-time, provably correct repairs. This approach bridges the gap between symbolic formal methods and data-driven AI, paving the way for more resilient, adaptable autonomous systems. Its success demonstrates a promising direction for future research in self-healing robots, safety-critical AI, and adaptive control, with implications spanning industrial automation, autonomous vehicles, and service robots. The methodology offers a scalable, flexible framework that can be extended to multi-agent systems and more complex environments, marking a substantial step forward in autonomous system robustness.
Technical Contribution
The paper introduces a novel hybrid framework that combines natural language processing via large language models with formal synthesis and verification techniques. Key innovations include the symbolic abstraction-to-NL translation, which leverages LLMs’ reasoning capabilities; the generation of repair skill candidates in a domain-specific language (DSL); and the integration of syntax and realizability checks using formal tools like Slugs. The feedback loop, based on counterexamples and safety analysis, iteratively refines repair candidates, ensuring correctness and robustness. This approach effectively handles large state spaces, overcoming traditional formal methods' scalability issues, and provides a practical, automated pipeline for runtime repair. Theoretical guarantees of correctness are maintained through formal verification, while the flexible candidate generation enhances adaptability and diversity.
Novelty
This work is the first to leverage large language models for real-time repair of high-level robot controllers in large state spaces. Unlike prior methods limited to offline synthesis or small environments, INPROVF employs a natural language interface to generate and refine repair skills dynamically. Its integration of symbolic abstraction, deep learning, and formal verification creates a scalable, automated repair pipeline, addressing the longstanding challenge of state explosion in formal methods. The iterative feedback mechanism based on counterexamples further distinguishes this approach, enabling continuous improvement and robustness. Overall, it represents a significant leap in autonomous repair technology, bridging AI and formal methods in a novel, practical manner.
Limitations
- The framework relies heavily on the reasoning and generation capabilities of pre-trained large language models, which may produce suboptimal or biased suggestions in highly complex or domain-specific scenarios.
- Verification still depends on symbolic tools like Slugs, which can face scalability issues as environment complexity grows exponentially, limiting real-time applicability in extremely large spaces.
- Natural language descriptions are subject to interpretation errors, potentially affecting the accuracy of the repair process, especially if descriptions are incomplete or ambiguous.
Future Work
Future research will focus on integrating multi-modal data (visual, tactile) to enhance environment understanding, employing reinforcement learning to optimize repair strategies, and extending the framework to multi-robot systems for collaborative repair. Additionally, efforts will be made to improve the efficiency of formal verification through incremental and parallelized algorithms, and to develop more robust natural language interfaces that reduce ambiguity. These advancements aim to make INPROVF suitable for deployment in real-world, safety-critical applications such as autonomous vehicles and industrial robots, ultimately moving toward fully autonomous, self-healing robotic systems.
AI Executive Summary
Deep Dive
Plain Language Accessible to non-experts
想象你在一个工厂里工作，工厂里有很多机器和流程。每个机器都有一套操作规程，但有时候因为突发情况，比如突然停电或机器故障，原本的操作规程就不能用了。这时，你需要快速想出新的操作方法，让机器继续工作，保证生产不受影响。INPROVF就像是这个工厂的智能助手，它能在机器出现问题时，利用之前学到的知识，快速提出新的操作方案，并确保这些方案是安全可靠的。它会不断试错，直到找到最合适的解决办法。这种方法结合了机器的规则和人工智能的智慧，让工厂的生产变得更灵活、更安全。
ELI14 Explained like you're 14
想象你在学校里玩一个游戏，游戏规则告诉你怎么做才能赢，但有时候规则会被打破，比如有人作弊或者出现意外情况。这时候，你需要想出新的策略来继续玩，确保自己还能赢。INPROVF就像是一个超级聪明的朋友，它能在你遇到问题时，帮你想出新的办法。它会先理解游戏的规则，然后用自己的聪明才智，提出一些新的动作或策略。接着，它会检查这些新策略是否合理，确保不会违反规则。如果不行，它会告诉你哪里出错，然后帮你改进。这样一来，无论遇到什么突发情况，你都能找到应对的方法，继续玩得开心又安全。
Abstract
This paper presents INPROVF, an automatic framework that combines large language models (LLMs) and formal methods to speed up the repair process of high-level robot controllers. Previous approaches based solely on formal methods are computationally expensive and cannot scale to large state spaces. In contrast, INPROVF uses LLMs to generate repair candidates, and formal methods to verify their correctness. To improve the quality of these candidates, our framework first translates the symbolic representations of the environment and controllers into natural language descriptions. If a candidate fails the verification, INPROVF provides feedback on potential unsafe behaviors or unsatisfied tasks, and iteratively prompts LLMs to generate improved solutions. We demonstrate the effectiveness of INPROVF through 12 violations with various workspaces, tasks, and state space sizes.