Task Decomposition and Self-Evaluation Mechanisms for Home Healthcare Robots Using Large Language Models

A system leveraging Large Language Models (LLMs), is proposed to address the limitations of current models primarily used for conversational purposes. While user interactions are excelled by ChatGPT, instability and safety issues are encountered when generating control codes for manipulator. This study focuses on the field of home healthcare services and proposes a new approach to control robots equipped with self-developed dexterous hand. The objective of this paper is to develop and evaluate a new system, Elderly Care GPT, aimed at improving the control of robots for home healthcare services. The system utilizes the Chain-of-Thought (CoT) prompting technique to enhance the accuracy of model-generated control codes. In conjunction with a self-evaluation mechanism, it ensures that the control codes undergo an accuracy evaluation before execution, thereby ensuring safety. Additionally, the system decomposes tasks into basic groups to improve the robot’s task execution capabilities. The performance of the model is evaluated across eight real-world scenarios, covering classic home care tasks. In real-world evaluations, Elderly Care GPT is compared with LLaMA3 and LLaMA3+ Prompt Words, and a comparison is made with mainstream models used for controlling robots on the market. The results show that Elderly Care GPT achieves a task success rate of up to 97.3%, demonstrating exceptional performance. This paper concludes that Elderly Care GPT outperforms current models used for robot control in home healthcare services, achieving high task success rates and effectively handling complex tasks. The use of Chain-of-Thought prompting, a self-evaluation mechanism, and task decomposition significantly contributes to the model’s success.