# VLM-driven Behavior Tree for Context-aware Task Planning

arXiv: https://arxiv.org/abs/2501.03968 · HTML: https://mlsanity.com/paper/2501.03968/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2025.

## Built on by (most cited first, 4 of 4)

- [Benchmarking LLM Privacy Recognition for Social Robot Decision Making](https://mlsanity.com/paper/2507.16124/) (2025)
- [Multimodal Behavior Tree Generation: A Small Vision-Language Model for Robot Task Planning](https://mlsanity.com/paper/2603.06084/) (2026)
- [Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision](https://mlsanity.com/paper/2604.02812/) (2026)
- [SharedKV-BT: Node-Local Typed Decisions for Behavior-Tree Agents](https://mlsanity.com/paper/2610.07327/) (2026)

## Abstract (arXiv)

The use of Large Language Models (LLMs) for generating Behavior Trees (BTs) has recently gained attention in the robotics community, yet remains in its early stages of development. In this paper, we propose a novel framework that leverages Vision-Language Models (VLMs) to interactively generate and edit BTs that address visual conditions, enabling context-aware robot operations in visually complex environments. A key feature of our approach lies in the conditional control through self-prompted visual conditions. Specifically, the VLM generates BTs with visual condition nodes, where conditions are expressed as free-form text. Another VLM process integrates the text into its prompt and evaluates the conditions against real-world images during robot execution. We validated our framework in a real-world cafe scenario, demonstrating both its feasibility and limitations.