VLM駆動型ビヘイビアツリーによる文脈認識型タスクプランニング
VLM-driven Behavior Tree for Context-aware Task Planning
視覚言語モデル(VLM)を用いて、視覚条件を考慮したビヘイビアツリーを対話的に生成・編集し、視覚的に複雑な環境での文脈認識型ロボット動作を実現するフレームワークを提案。実世界のカフェ環境で検証した。
著者: Naoki Wake, Atsushi Kanehira, Jun Takamatsu, Kazuhiro Sasabuchi, Katsushi Ikeuchi
分類: cs.RO, cs.AI, cs.CV, cs.HC
原文アブストラクト
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.
