SafeEmbodAI: a Safety Framework for Mobile Robots in Embodied AI Systems

TL;DR

SafeEmbodAI is a safety framework that enhances the security and robustness of mobile robots in embodied AI systems by mitigating malicious commands and improving navigation safety in complex environments.

Contribution

This paper introduces SafeEmbodAI, a novel safety framework integrating secure prompting, state management, and safety validation for mobile robots in embodied AI systems.

Findings

Effective mitigation of malicious command threats.

267% performance increase in complex environments under attack.

Improved safety and navigation performance in simulated tests.

Abstract

Embodied AI systems, including AI-powered robots that autonomously interact with the physical world, stand to be significantly advanced by Large Language Models (LLMs), which enable robots to better understand complex language commands and perform advanced tasks with enhanced comprehension and adaptability, highlighting their potential to improve embodied AI capabilities. However, this advancement also introduces safety challenges, particularly in robotic navigation tasks. Improper safety management can lead to failures in complex environments and make the system vulnerable to malicious command injections, resulting in unsafe behaviours such as detours or collisions. To address these issues, we propose \textit{SafeEmbodAI}, a safety framework for integrating mobile robots into embodied AI systems. \textit{SafeEmbodAI} incorporates secure prompting, state management, and safety validation mechanisms to secure and assist LLMs in reasoning through multi-modal data and validating responses. We designed a metric to evaluate mission-oriented exploration, and evaluations in simulated environments demonstrate that our framework effectively mitigates threats from malicious commands and improves performance in various environment settings, ensuring the safety of embodied AI systems. Notably, In complex environments with mixed obstacles, our method demonstrates a significant performance increase of 267\% compared to the baseline in attack scenarios, highlighting its robustness in challenging conditions.