Generating Human Daily Activities with LLM for Smart Home Simulator Agents

This paper presents a novel approach to smart home simulation by integrating Large Language Models (LLMs) for generating human daily schedules and activities of smart home simulator agents. The use of LLMs aims to reduce the complexity of user settings and increase the variety and generality of generated activities. This study explores the potential of LLMs to simulate human-like activity-generating by exploiting their experiential knowledge and adaptability. In addition, this paper discusses fine-tuning techniques for LLMs to optimize their performance within a simulator. By utilizing LoRA and task-specific fine-tuning datasets, while maintaining the ability to generate proper activities, we achieve as much as 4.3% performance improvement about the number of queries. Overall, the integration of LLMs into smart home simulators offers promising opportunities for enhancing the intelligence and adaptability of virtual agents in smart home environments.