LLM-BT: Performing Robotic Adaptive Tasks based on Large Language Models and Behavior Trees

TY - GEN

T1 - LLM-BT

T2 - 2024 IEEE International Conference on Robotics and Automation, ICRA 2024

AU - Zhou, Haotian

AU - Lin, Yunhan

AU - Yan, Longwu

AU - Zhu, Jihong

AU - Min, Huasong

N1 - Publisher Copyright: © 2024 IEEE.

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85197618191&partnerID=8YFLogxK

U2 - 10.1109/ICRA57147.2024.10610183

DO - 10.1109/ICRA57147.2024.10610183

M3 - Conference contribution

AN - SCOPUS:85197618191

T3 - Proceedings - IEEE International Conference on Robotics and Automation

SP - 16655

EP - 16661

BT - 2024 IEEE International Conference on Robotics and Automation, ICRA 2024

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 13 May 2024 through 17 May 2024

ER -