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

arXiv: https://arxiv.org/abs/2404.05134 · HTML: https://mlsanity.com/paper/2404.05134/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2024.

## Built on by (most cited first, 14 of 14)

- [Robot Operation of Home Appliances by Reading User Manuals](https://mlsanity.com/paper/2505.20424/) (2025)
- [ELHPlan: Efficient Long-Horizon Task Planning for Multi-Agent Collaboration](https://mlsanity.com/paper/2509.24230/) (2025)
- [Adaptive Manipulation using Behavior Trees](https://mlsanity.com/paper/2406.14634/) (2024)
- [AINav: Large Language Model-Based Adaptive Interactive Navigation](https://mlsanity.com/paper/2503.22942/) (2025)
- [From Prompts to Pavement: LMMs-based Agentic Behavior-Tree Generation Framework for Autonomous Vehicles](https://mlsanity.com/paper/2601.12358/) (2026)
- [Multimodal Behavior Tree Generation: A Small Vision-Language Model for Robot Task Planning](https://mlsanity.com/paper/2603.06084/) (2026)
- [LEMMo-Plan: LLM-Enhanced Learning from Multi-Modal Demonstration for Planning Sequential Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2409.11863/) (2024)
- [MinionsLLM: a Task-adaptive Framework For The Training and Control of Multi-Agent Systems Through Natural Language](https://mlsanity.com/paper/2508.08283/) (2025)
- [RoboBRIDGE: A Modular Framework for Bridging Policies to Robust Real-World Robotic Agents](https://mlsanity.com/paper/2607.27881/) (2026)
- [Interpretable Robot Control via Structured Behavior Trees and Large Language Models](https://mlsanity.com/paper/2508.09621/) (2025)
- [Instruct2Act: From Human Instruction to Actions Sequencing and Execution via Robot Action Network for Robotic Manipulation](https://mlsanity.com/paper/2602.09940/) (2026)
- [From Dialogue to Execution: Mixture-of-Agents Assisted Interactive Planning for Behavior Tree-Based Long-Horizon Robot Execution](https://mlsanity.com/paper/2603.01113/) (2026)
- [ORDER: A Fictitious-World Benchmark for Domain-Adaptive Embodied AI](https://mlsanity.com/paper/2609.22285/) (2026)
- [Towards An LLM-Driven Unified Conversion Framework for BT and FSM in Autonomous Intelligent Systems](https://mlsanity.com/paper/2609.29228/) (2026)

## Abstract (arXiv)

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.