Trustworthy Robot Behavior Tree Generation Based on Multi-Source Heterogeneous Knowledge Graph

Abstract

In robotics, the design of robot behavior trees generally requires roboticists to comprehensively and customizable consider all the relevant factors including the robot hardware capabilities, task descriptions, etc, posing great challenges for design quality and efficiency. The mainstream practice of BT design paradigm has been utilizing the BT component framework to develop task-specific BT structures manually. In contrast, the latest advances in Generative Pretrained Transformers (GPTs) have also opened up the possibility of BT design automation. However, these approaches generally show low efficiency or are less trustworthy for complex robot task goals due to time-consuming manual design and unreliable GPT reasoning. To solve the above limitations, this paper proposes a novel knowledge-driven approach that develops a specialized knowledge graph from multi-sourced and heterogeneous highquality robot knowledge to reason out a trustworthy robot plan for achieving complex task goals. Then we present the plan transformation and BT merging algorithms to automatically generate the plan-level BT structure. The comparative experiment results have shown that our approach can generate highquality and trustworthy BT structure regarding the task plan accuracy and consistency, as well as the BT generation time, compared with the manual design and GPT-based approaches.