LLiBTree: An Assessment of Usability and Trust of LLM-integrated Behavior Tree Nodes for Robot Programming

Abstract

As robot programming becomes increasingly important in enabling non-expert users to customize autonomous systems across domains such as manufacturing, healthcare, and service industries, there is a growing demand for methods that are both intuitive and support users’ perceived trust. While natural language programming (NLP) lowers the barrier to entry by allowing users to describe robot behavior in everyday language, it often lacks structural transparency—particularly when paired with opaque deep learning models. To address this challenge, we present LLiBTree, a novel robot programming framework that integrates Large Language Models (LLMs) directly into Behavior Trees (BTs) as executable nodes. This approach allows users to iteratively define and refine robot behavior via natural language while preserving the modularity and clarity of visual programming. By embedding the LLM within the BT, users can generate and replace sub-trees in response to natural language instructions, enabling a seamless blend of language-based flexibility and structured visual logic. We evaluate LLiBTree through a controlled user study, comparing it with both natural language-only and conventional visual programming methods. The results show that the PreGeneratedLLiBTree condition yields significantly higher perceived trust, while LLiBTree conditions achieve usability that is non-inferior to natural language-only programming and improve users’ sense of agency, suggesting that LLiBTree offers a practical approach for end-user robot programming.