From Language to Deployment: Offline Optimization and Ontology-Guided Behavior Tree Generation for Transparent Robot Applications

Abstract

Automatically generating robot applications from natural language promises to lower the barrier to automation, but remains difficult in domains that demand reliability and transparency, such as industrial assembly or collaborative manipulation. End-to-end policies and a large language model(LLM)-based planners can map instructions to robot behaviors, but they often lack interpretability and provide limited assurance of correctness. We present a framework that composes applications from modular, application-independent atomic skills expressed as Behavior Trees (BTs). BTs are constructed and validated against an ontology-level dual graph to enforce control-flow and data-flow consistency before execution, ensuring transparency and structural correctness. Application-level parameters are optimized offline in simulation using Monte Carlo Tree Search guided by LLM-derived priors. Rather than serving as a runtime optimizer, this process systematically explores interdependent parameters, producing a dataset of reliable parameterizations that can support future gating mechanisms for online adaptation. The framework is validated in a physical robotic setup, demonstrating transparent and consistent offline generation of deployable applications, and laying the foundation for adaptive, real-time systems.