Core Insights - The article discusses the integration of Large Language Models (LLMs) with Behavior Trees (BT) to enhance robotic task execution and adaptability in the presence of external disturbances [1][2][12]. Group 1: LLM and BT Integration - LLMs are utilized to interpret user commands into behavior trees that include task goal conditions [2]. - The combination of LLMs and BT allows for fewer calls to LLMs while managing external disturbances through an action database [2][12]. - A human-in-the-loop learning mechanism is proposed to refine the knowledge generated by LLMs, ensuring safety and adaptability in robotic operations [5][7]. Group 2: Human-in-the-Loop Learning Mechanism - The mechanism involves designing a context for LLMs that includes prompt engineering, manipulation primitives (MPs), and an action database [5]. - User interactions guide LLMs to correct and enhance the generated action knowledge, which is then added to the action database after user confirmation [7][12]. - The generated action knowledge consists of preconditions, postconditions, and a set of MPs, implemented in BT format [7]. Group 3: Task Evaluation and Performance - Eight tasks were designed to evaluate the proposed method, categorized into three difficulty levels: Easy, Medium, and Hard [9]. - The proposed method achieved a success rate of over 80% across the tasks, significantly outperforming baseline methods that lacked human interaction [12]. - The adaptability of the generated action knowledge was tested against external disturbances, achieving a success rate exceeding 70% [14]. Group 4: Generalization and Future Improvements - The generated action knowledge demonstrated good generalization capabilities, with success rates over 70% for certain tasks involving new objects [17]. - However, some tasks had success rates below 40% due to the inapplicability of MPs parameters to new objects, indicating a need for fine-tuning before application [17]. - Overall, the proposed human-in-the-loop learning mechanism enhances robotic learning performance, enabling robots to complete tasks and respond to external disturbances effectively [18].