Large language model-based embodied intelligence task planning: from single-agent to multi-agent

With the development of artificial intelligence, embodied intelligence and task planning have gradually become research hotspots. Traditional task planning methods lack flexibility when facing unpredictable environments, while large language models, with their powerful language understanding and multimodal capabilities, provide more comprehensive task planning solutions for intelligent agents, which offers new possibilities for addressing this issue. This paper reviews task planning methods based on large language models, covering different strategies in both single-agent and multi-agent contexts. Several representative frameworks and their performance and potential in practical applications are discussed. Specifically, this paper introduces single-agent large language model task planning methods, such as end-to-end planning, staged planning, and dynamic planning, and multi-agent large language model task planning methods, such as centralized planning, distributed planning, and hybrid planning. It also analyzes how these methods combine with reinforcement learning, multimodal perception, and other techniques to optimize the planning process. In addition, the paper discusses the characteristics, limitations, and challenges of large language model-based embodied intelligence task planning, and outlines the future development directions. This paper aims to provide valuable insights for designing more flexible and adaptive next-generation embodied intelligent systems.