Repair2Skill: A Vision–Language–Action Framework for Robotic Furniture Repair - Tripathi, Mukesh Mani; Xin, Hunag; Li, Qingqing (Proceedings of the AAAI Symposium Series, 2026)
Repair tasks differ fundamentally from robotic assembly, requiring fault localization, reverse dependency reasoning, and safe interaction with structurally degraded objects. While recent vision-language models have shown promise for high-level task reasoning, applying them to repair scenarios demands careful integration with perception, dependency modeling, and execution constraints. This paper presents Repair2Skill, a vision–language–action framework for robotic furniture repair that focuses on repair planning and simulation-based execution validation. The system detects damaged furniture components using a lightweight SSDLite-MobileNetV3 detector trained on procedurally generated synthetic damage data, constructs repair-oriented dependency graphs, and generates structured repair plans using a constrained large language model. Repair plans are restricted to a fixed action vocabulary and strict JSON schema to ensure deterministic parsing and feasibility. Generated plans are validated and visualized in a PyBullet simulation environment using kinematic action primitives, enabling interpretable verification of action ordering and dependency resolution without contact-rich physical manipulation. Experimental results demonstrate reliable part-damage association, achieving 76.8% mAP on real images, 100% valid repair plan generation under strict constraints, and stable simulation based validation across diverse repair scenarios. These results suggest that lightweight, constrained vision-language pipelines can support autonomous repair planning in bounded domains and provide a foundation for future work on physically grounded robotic repair.