Optimizing and Attacking Embodied Intelligence: Instruction Decomposition and Adversarial Robustness

Embodied intelligence, which enables agents to interact with the physical world, has gained significant attention for its real-world applications. However, these systems face two key challenges: optimizing task instructions and addressing vulnerabilities to adversarial attacks. We propose an AI agent integrating a large language model (LLM), a CLIP model, and two contextual databases. Additionally, we introduce an adversarial attack framework that manipulates prompt attention by replacing high-attention words and appending adversarial suffixes, inducing hazardous behaviors while maintaining visual and semantic plausibility. Our work advances instruction optimization and highlights vulnerabilities in embodied intelligence systems. Experimental results show that our optimization approach generates precise, real-world instructions. At the same time, our adversarial attack achieves higher success rates with reduced computational overhead, advanced optimization, and robustness in embodied intelligence systems.