Application of Large Language Models in Embodied Artificial Intelligence - Li, Zitian (Transactions on Computer Science and Intelligent Systems Research, 2024)
The convergence of Artificial Intelligence (AI) and robotics has led to the emergence of embodied AI, where intelligent systems equipped with sensors and actuators interact with the physical world and operate alongside humans. These systems are transforming industries such as autonomous driving, healthcare, and household assistance. However, despite extensive research, embodied AI systems face significant limitations, including poor generalization and performance degradation in complex environments, hindering their commercialization. Recent developments in Large Language Models (LLMs) present new opportunities to address the above challenges. This study aims to explore the integration of LLMs into embodied AI systems, highlighting their potential to enhance scene understanding, reasoning, and planning capabilities. The paper provides a detailed review of LLMs’ applications in embodied AI, demonstrating how these models can improve the robustness and adaptability of AI systems. Additionally, the study examines the limitations of LLMs, such as hallucinations and efficiency challenges, and discusses potential solutions to mitigate these issues. Through an in-depth analysis of LLM-powered enhancements in embodied AI, this research underscores the transformative impact of LLMs on intelligent systems. By addressing current limitations and implementing innovative solutions, LLMs can significantly advance the field of embodied AI, paving the way for more versatile and intelligent systems that can operate effectively in diverse real-world environments.