  Large Language Models (LLMs) have been widely utilized to perform complex
robotic tasks. However, handling external disturbances during tasks is still an
open challenge. This paper proposes a novel method to achieve robotic adaptive
tasks based on LLMs and Behavior Trees (BTs). It utilizes ChatGPT to reason the
descriptive steps of tasks. In order to enable ChatGPT to understand the
environment, semantic maps are constructed by an object recognition algorithm.
Then, we design a Parser module based on Bidirectional Encoder Representations
from Transformers (BERT) to parse these steps into initial BTs. Subsequently, a
BTs Update algorithm is proposed to expand the initial BTs dynamically to
control robots to perform adaptive tasks. Different from other LLM-based
methods for complex robotic tasks, our method outputs variable BTs that can add
and execute new actions according to environmental changes, which is robust to
external disturbances. Our method is validated with simulation in different
practical scenarios.
