Jingtao Qi — accepted AI-conference papers

2025

BeSimulator: A Large Language Model Powered Text-based Behavior Simulator

Jianan Wang, Bin Li, Jingtao Qi, Xueying Wang, Fu Li, Lihanxun

EMNLP 2025

Traditional robot simulators focus on physical process modeling and realistic rendering, often suffering from high computational costs, inefficiencies, and limited adaptability. To handle this issue, we concentrate on behavior simulation in robotics to analyze and validate the logic behind robot beh

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2025

Code-BT: A Code-Driven Approach to Behavior Tree Generation for Robot Tasks Planning with Large Language Models

Siyang Zhang, Bin Li, Jingtao Qi, Xueying Wang, Fu Li, Jianan Wang +2

IJCAI 2025

Behavior trees(BTs) provide a systematic and structured control architecture extensively employed in game AI and robotic behavior control, owing to their modularity, reactivity, and reusability. Nonetheless, manual BTs design requires significant expertise and becomes inefficient as task complexity

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