# A Survey of Robot Intelligence with Large Language Models
## 4 Language Models for Robotic Intelligence
### High-Level Planning (Including Decision-Making and Reasoning)  
The abstraction and generalization capabilities of LLMs offer effective methodologies for high-level planning tasks in robotic systems. Leveraging these capabilities, various research outcomes have been realized in the fields of planning, decision-making, reasoning, and behavior trees within robotics. [PERSON] [149] introduced Statler, a framework designed to provide LLMs with an explicit world state representation through a continuously maintained'memory'. The core of Statler consisted of two components: the world model reader and the world model writer. These components interacted with and sustained the world state. The world model reader interpreted user commands and generated executable code based on the current state representation, while the world model writer updated the system's state according to execution outcomes. By facilitating access to the world state'memory', Statler improved LLMs' ability to reason about planning tasks with extended time horizons, overcoming limitations imposed by context length. [PERSON] [150], shown in Figure 7, introduced EmbodiedGPT, a model specifically designed for Embodied AI, which leverages LLMs. This framework processes visual observations and natural language to establish long-term plans and execute tasks in real-time. EmbodiedGPT utilizes pre-trained vision transformers and the LLaMA language model to encode visual features and map them to the language modality. The generated plan was subsequently converted into specific task commands using general visual tokens, encoded by the vision model. The framework's functionality comprises (1) encoding current visual features, (2) mapping visual features to the language modality via attention-based interactions between visual tokens and text queries or learnable embedded queries, (3) generating plans with the LLaMA language model and translating them into specific task commands, and (4) querying the encoded visual tokens from the vision model and translating them into low-level control commands through a downstream policy network for task execution. Experimental results, utilizing the MS-COCO dataset, revealed that EmbodiedGPT excels in object recognition and understanding spatial relationships. Notably, implement  
Figure 6: Pre-trained LLMs can act as general sequence modelers, and their abilities were assessed in sequence transformation, completion, and improvement [148]. ing a closed-loop design and a \"chain-of-thought\" training mode significantly enhanced EmbodiedGPT's performance. These results demonstrate that EmbodiedGPT effectively handles various autonomous tasks, exhibiting superior capability in object recognition, understanding spatial relationships, and generating logical, executable plans. [PERSON] [151] introduced the language-model-based commonsense reasoning (LMCR) framework to assist robots in comprehending incomplete natural language instructions. This framework enabled robots to receive instructions in natural language from humans, observe their surroundings, and employ a commonsense reasoning method to autonomously infer missing information. LMCR utilized a model of commonsense reasoning learned from web-based text materials, allowing robots to understand incomplete instructions and autonomously execute tasks. The framework comprised three main functions: language understanding, commonsense reasoning, and action planning. In language understanding, LMCR translated human natural language instructions into a form interpretable by robots, parsing them into verb frames to convert them into executable structures. During the commonsense reasoning phase, the robot analyzed surrounding objects and employed a language model trained on large-scale unstructured text materials to fill in the missing details from the instructions. This model identified the most suitable verb frame to complete the gaps.