LLMs Enhance Robotic Anomaly Detection

By Soham NandiReviewed by Susha Cheriyedath, M.Sc.Jul 17 2024

In an article recently submitted to the arxiv* server, researchers discussed using large language models (LLMs) to detect and address anomalies in robotic systems. They presented a two-stage framework, a fast anomaly classifier that operated in an LLM embedding space and a slower, reasoning fallback system. This approach ensured the safe and trustworthy operation of dynamic robotic systems, like quadrotors or autonomous vehicles, while mitigating computational costs and incorporating LLM judgment into control frameworks.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Background

Autonomous robotic systems are nearing widespread deployment. However, their limited training datasets often do not encompass the full range of real-world scenarios, resulting in unexpected failures. Previous research has shown that foundation models (FMs), especially LLMs, have strong zero-shot reasoning capabilities and can perform complex tasks, identify and correct failures, and assess safety hazards without explicit training.

However, their integration into real-time, safety-critical applications is challenging due to computational constraints and response times. Traditional methods have focused on quasi-static settings or offline applications, which are not feasible for dynamic, agile robots like quadrotors.

The present paper introduced AESOP, a two-stage anomaly detection and reactive planning framework that leveraged LLMs for rapid anomaly detection and methodical reasoning. AESOP addressed the computational expense and latency issues by combining fast embedding-based anomaly detection with slower, generative reasoning, ensuring safe and timely interventions in dynamic environments.

Problem Definition and Approach

The researchers focused on enhancing robotic safety by creating a runtime monitor for a robot's control system. The robot's control aimed to minimize objectives like distance to a landing zone while avoiding collisions. The monitor detected unsafe conditions not captured by standard state variables, like environmental hazards.

Using a dataset of safe, nominal observations, the system identified rare, dangerous scenarios and engaged in safety-preserving interventions. For example, if a quadrotor's landing zone was unsafe, it redirected to an alternate safe zone, ensuring reliable operation even in unexpected situations.

Proposed Methodology

The proposed approach addressed the challenge of handling unforeseen failures in robotic systems by integrating anomaly detection and reasoning capabilities. Firstly, it acknowledged the limitations of traditional engineering approaches in capturing all possible failure scenarios. Instead, it proposed using generalist FM that could comprehend a robot's environment holistically.

The approach involved a two-stage monitoring pipeline that included fast anomaly detection and slow generative reasoning. Fast detection quickly identified deviations from normal operation using anomaly scores computed from prior, reliable data. These scores assessed how current observations differed from past experiences stored as embeddings. Slow reasoning engaged an LLM to generate informed decisions about potential safety interventions. This involved converting visual observations into textual descriptions, allowing for zero-shot assessments of safety risks and necessary interventions.

To operationalize these findings, a planning algorithm employed model predictive control (MPC) to ensure both nominal trajectory execution and feasible paths to predefined recovery sets. This ensured that even in the event of an anomaly, the system could swiftly and safely navigate towards a predefined safe state or action.

Experimental Evaluation of Anomaly Detection in Robotics

The researchers conducted a set of experiments to test five key hypotheses regarding anomaly detection in robotic systems. The first hypothesis posited that the fast embedding-based anomaly detector outperformed generative reasoning-based approaches in identifying anomalies through semantic differences relative to prior experiences. The second hypothesis suggested that effective anomaly detection could be achieved using smaller models, which were more cost-effective, rather than relying on high-capacity generative models.

The experiments involved four main tests. The first assessed the performance of the anomaly detector in three artificial robotic environments—warehouse manipulation, autonomous vehicles, and vertical takeoff and landing (VTOL) aircraft. The second tested the full integration of the proposed approach in a real-time control simulation of an agile drone. The third involved a hardware experiment with a quadrotor, demonstrating practical viability, and the fourth explored the application of the runtime monitor in a realistic self-driving environment using both multi-modal and language embeddings.

Results indicated that while the fast anomaly detector exhibited strong performance, generative reasoning approaches, particularly those utilizing LLMs, effectively assessed whether detected anomalies warranted safety interventions. This integration of fast detection with slow reasoning supported real-time robotic control, further validating the proposed framework's efficacy.
The findings highlighted the potential for leveraging embedding-based anomaly detection alongside generative reasoning for improved safety in robotics, suggesting a promising direction for future research in end-to-end anomaly detection methodologies.

Conclusion

In conclusion, the proposed framework harnessed generalist FMs to enable safe and real-time control of agile robotic systems facing diverse anomalies. By integrating fast anomaly detection in LLM embedding spaces with methodical generative reasoning, the researchers ensured prompt and informed safety responses.

The experiments validated the effectiveness of embedding-based anomaly detection, even with modest computational resources, and highlighted the potential of LLMs for enhancing robustness in handling unforeseen scenarios. Future research could focus on optimizing LLM inference latencies and refining fallback strategies for further improvements in autonomous robot performance and reliability.

*Important notice: arXiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.