BTGenBot-2: 小型言語モデルによる効率的なビヘイビアツリー生成
BTGenBot-2: Efficient Behavior Tree Generation with Small Language Models
自然言語のタスク指示とロボットの行動プリミティブから実行可能なビヘイビアツリーをXMLで直接生成する1Bパラメータのオープンソース小型言語モデルを提案し、標準ベンチマークを構築した。
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著者: Riccardo Andrea Izzo, Gianluca Bardaro, Matteo Matteucci
分類: cs.RO
原文アブストラクト
Recent advances in robot learning increasingly rely on LLM-based task planning, leveraging their ability to bridge natural language with executable actions. While prior works showcased great performances, the widespread adoption of these models in robotics has been challenging as 1) existing methods are often closed-source or computationally intensive, neglecting the actual deployment on real-world physical systems, and 2) there is no universally accepted, plug-and-play representation for robotic task generation. Addressing these challenges, we propose BTGenBot-2, a 1B-parameter open-source small language model that directly converts natural language task descriptions and a list of robot action primitives into executable behavior trees in XML. Unlike prior approaches, BTGenBot-2 enables zero-shot BT generation, error recovery at inference and runtime, while remaining lightweight enough for resource-constrained robots. We further introduce the first standardized benchmark for LLM-based BT generation, covering 52 navigation and manipulation tasks in NVIDIA Isaac Sim. Extensive evaluations demonstrate that BTGenBot-2 consistently outperforms GPT-5, Claude Opus 4.1, and larger open-source models across both functional and non-functional metrics, achieving average success rates of 90.38% in zero-shot and 98.07% in one-shot, while delivering up to 16x faster inference compared to the previous BTGenBot.
