DiaGBT: An Explainable and Evolvable Robot Control Framework using Dialogue Generative Behavior Trees

Manipulating robots using natural language is the preferred way for non-technical specialists. The challenge lies in reliability and adaptability especially when robots operate in unstructured surroundings. In this paper, we propose a novel framework called Dialogue Generative Behavior Trees (DiaGBT). Natural language instructions from human operators are transformed into behavior trees (BTs) and further executed by robots. Compared to the emerging Large Language Models (LLMs), DiaGBT is comparable in terms of semantic understanding but more lightweight, since the parsing rules are produced by LLM but tailored for task-correlated instructions. Besides, DiaGBT allows multi-round human-robot interaction, where robots learn reusable skills in real time. For evaluation, we generate a dataset with 4k instruction-BT pairs covering 4 different scenarios. On average, DiaGBT reaches over 90% parsability and 80% plausibility. Similar results on the VEIL-500 dataset outperform the current state of the art.

Authors

• Jinde Liang
• Yuan Chang
• Qian Wang
• Yanzhen Wang
• Xiaodong Yi 0002

Keywords

• Large language models
• Natural languages
• Semantics
• Robot control
• Human-robot interaction
• Real-time systems
• Behavioral sciences
• Reliability
• Intelligent robots
• Behavior Trees
• Natural Language
• Human Operator
• User Experience
• Root Node
• Semantic Similarity
• Disambiguation
• Type Of Education
• Leaf Node
• Learning Rule
• Incremental Test
• Node Activity
• Relevant Modules
• Robotic Tasks
• Semantic Space
• Control Nodes
• Semantic Vectors
• Unreal Engine