Real-Time Anomaly Detection and Reactive Planning with Large Language Models (2407.08735v1)

Abstract: Foundation models, e.g., LLMs, trained on internet-scale data possess zero-shot generalization capabilities that make them a promising technology towards detecting and mitigating out-of-distribution failure modes of robotic systems. Fully realizing this promise, however, poses two challenges: (i) mitigating the considerable computational expense of these models such that they may be applied online, and (ii) incorporating their judgement regarding potential anomalies into a safe control framework. In this work, we present a two-stage reasoning framework: First is a fast binary anomaly classifier that analyzes observations in an LLM embedding space, which may then trigger a slower fallback selection stage that utilizes the reasoning capabilities of generative LLMs. These stages correspond to branch points in a model predictive control strategy that maintains the joint feasibility of continuing along various fallback plans to account for the slow reasoner's latency as soon as an anomaly is detected, thus ensuring safety. We show that our fast anomaly classifier outperforms autoregressive reasoning with state-of-the-art GPT models, even when instantiated with relatively small LLMs. This enables our runtime monitor to improve the trustworthiness of dynamic robotic systems, such as quadrotors or autonomous vehicles, under resource and time constraints. Videos illustrating our approach in both simulation and real-world experiments are available on this project page: https://sites.google.com/view/aesop-LLM.

• The paper introduces a two-stage reasoning framework that swiftly detects anomalies with fast LLM embeddings and generates detailed safety interventions using generative LLMs.
• The methodology leverages efficient small LLMs for real-time binary anomaly detection, outperforming heavier autoregressive models in resource-constrained settings.
• Extensive experiments on quadrotors and autonomous vehicles demonstrate the framework's reliability in maintaining safety during dynamic and unpredictable operations.

Real-Time Anomaly Detection and Reactive Planning with LLMs

This paper explores the integration of LLMs into the real-time anomaly detection and reactive planning processes for robotic systems. Specifically, it proposes using LLMs for detecting out-of-distribution (OOD) failures and ensuring safe navigation and operational continuity in dynamic environments.

Key Contributions

1. Two-Stage Reasoning Framework: The core contribution of this work is the introduction of a two-stage reasoning framework. The first stage involves a fast binary anomaly classifier that operates in the embedding space of an LLM, aiming to detect anomalies in real-time. If an anomaly is flagged, the second stage, which is slower, employs the reasoning capabilities of generative LLMs to determine appropriate fallback actions.
2. Embedding-Based Anomaly Detection: The paper highlights the efficacy of using embeddings from relatively small LLMs for fast anomaly detection. This method outperforms autoregressive reasoning approaches with higher-capacity models such as GPT-4, demonstrating that smaller models can still offer significant utility in resource-constrained environments.
3. Hierarchical Multi-Contingency Planning: The paper proposes a model predictive control (MPC) strategy that integrates both fast and slow reasoning stages. This strategy ensures the dynamic feasibility of multiple trajectory plans, maintaining safety throughout the LLM’s latency period required to reason about the anomaly.

Theoretical Framework and Methodology

The proposed approach integrates the LLM-based reasoning processes into a model predictive control (MPC) framework tailored for safety-critical robotic systems. The two main stages in the anomaly detection and response cycle are:

• Fast Anomaly Detection: This involves using a binary classifier based on the LLM's embedding space. The classifier rapidly assesses whether the current observation deviates significantly from the nominal dataset D_nom.
• Slow Generative Reasoning: Upon detecting an anomaly, this stage utilizes autoregressive LLMs to generate an assessment of the anomaly's hazard and decide on the appropriate safety-preserving intervention. The key challenge addressed here is managing the LLM’s inference latency to ensure real-time applicability.

Practical Implications and Experimental Validation

The paper presents extensive experiments across synthetic text-based domains, simulated and real-world environments, showcasing the latency-sensitive integration of LLM-based reasoning into dynamic, agile robotic systems such as quadrotors and autonomous vehicles.

Experimental Insights:

1. Synthetics: The paper demonstrates that embedding-based anomaly detection achieves high accuracy across multiple robotic domains, outperforming generative reasoning approaches in anomaly detection tasks.
2. Quadrotor Simulation and Hardware: Through both simulated and real-world quadrotor experiments, the paper validates that the system can efficiently switch between nominal operations and safety-preserving interventions, leveraging the fast anomaly detector and the slower, detailed reasoning from LLMs.
3. Autonomous Vehicles: The paper extends its findings to autonomous vehicles in the CARLA simulator, illustrating the robust performance of LLMs in detecting complex, semantically rich anomalies in real-time scenarios.

Quantitative Performance and Safety Guarantees

The approach ensures that the autonomous system maintains high safety standards under runtime anomalies, with strong numerical results validating the reliability and effectiveness of the proposed method. The theoretical aspects are anchored in ensuring that the system follows consistent safety protocols without compromising performance, even in resource and time-constrained environments.

Future Directions

This work opens avenues for further research in several areas:

• Streamlining LLM Integration: Enhancing methods to mitigate LLM inference latency and exploring more efficient algorithmic strategies for integrating multi-modal embeddings.
• Broader Application Domains: Extending the LLM-based real-time anomaly detection and planning framework to a wider range of robotic systems and complex environments.
• Adaptive Anomaly Detection: Investigating methods for continual learning and adaptation based on historical anomaly assessments to improve long-term robustness.

Conclusion

This paper provides a comprehensive paper on enhancing the robustness and safety of autonomous robotic systems through the integration of LLMs for real-time anomaly detection and reactive planning. By leveraging both the computational efficiency of smaller embedding-based models and the generalist reasoning capabilities of generative LLMs, it offers a promising direction for the practical application of AI in dynamic, safety-critical environments. The results suggest significant opportunities to further improve the reliability and operational safety of autonomous systems in unpredictable real-world scenarios.