多模态行为树生成：面向机器人任务规划的小型视觉语言模型

Computer Science > Robotics arXiv:2603.06084 (cs) [Submitted on 6 Mar 2026 (v1), last revised 16 Sep 2026 (this version, v2)] Title:Multimodal Behavior Tree Generation: A Small Vision-Language Model for Robot Task Planning Authors:Riccardo Andrea Izzo, Cristiano Battistini, Gianluca Bardaro, Matteo Matteucci View a PDF of the paper titled Multimodal Behavior Tree Generation: A Small Vision-Language Model for Robot Task Planning, by Riccardo Andrea Izzo and 3 other authors View PDF HTML (experimental) Abstract:Large language models have been widely used for robotic task planning, often taking advantage of representations such as Behavior Trees (BTs). Vision-Language Models (VLMs) have extended these works by grounding the generated plans in the observed scene. However, existing methods are either text-only or rely on large proprietary VLMs, while no dataset pairs visual observations and task instructions with executable and ROS2-compatible BTs. We address this gap with a multi-stage teacher pipeline that converts 1,622 Open X-Embodiment episodes into an augmented multimodal BT dataset containing 2,433 examples. We use this dataset to fine-tune compact and open-source VLMs, ranging from 500M to 4B parameters, using parameter-efficient fine-tuning (PEFT). We then evaluate the generated BTs offline in terms of syntactic correctness and by executing them on 15 household tasks in BEHAVIOR-1K. Our best model, Gemma-3 4B, achieves perfect BT validity and an 87% success rate, outperforming Claude Opus 4.8 and approaching GPT-5, while running locally. Finally, our ablation studies show that adding visual observations increases task success from 40% to 87%, while data augmentation increases BT validity from 65% to 100%. Subjects: Robotics (cs.RO) Cite as: arXiv:2603.06084 [cs.RO] (or arXiv:2603.06084v2 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2603.06084 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Riccardo Andrea Izzo [view email] [v1] Fri, 6 Mar 2026 09:36:29 UTC (5,085 KB) [v2] Wed, 16 Sep 2026 11:13:38 UTC (2,810 KB) Full-text links: Access Paper: View a PDF of the paper titled Multimodal Behavior Tree Generation: A Small Vision-Language Model for Robot Task Planning, by Riccardo Andrea Izzo and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)