IEEE Transactions on Automation Science and Engineering(T-ASE)に当研究室の論文が採録

■書誌情報
Zhongchao Zhou, Yuxi Lu, Yaonan Zhu, Yifei Zhao, Qian Niu, Bin He, Liang He, Wenwei Yu, Yusuke Iwasawa: LLM-guided Adaptive Compensator: Bringing Adaptivity to Robotic Feedback Control with Large Language Model, IEEE Transactions on Automation Science and Engineering (T-ASE), 2026
■概要
Recent advances in code generation and reasoning have enabled the growing use of large language models (LLMs) in robotics. The design of traditional robotic feedback controllers often requires extensive expert knowledge and iterative computational effort, motivating the exploration of LLM-assisted controller development. However, existing efforts are largely restricted to overly simplified systems, provide limited comparison with human-designed controllers, and lack validation on real-world robotic platforms. To address these gaps, this work investigates the use of LLMs in robotic feedback control by focusing on adaptive control and proposing an LLM-guided adaptive compensator. Instead of generating a complete controller from scratch, the LLM is guided to design a compensator that modulates the response of an unknown system to match that of a predefined reference system, thereby achieving adaptivity. The proposed approach is evaluated using five adaptive controllers on a two-degree-of-freedom (DoF) soft robot and a one-DoF shoulder joint of a humanoid robot. Both simulation and real-world experiments demonstrate that the LLM-guided adaptive compensator achieves performance comparable to four conventional adaptive controllers. Lyapunov-based analysis further establishes the stability and generalization capability of the proposed compensator, while analysis of the reasoning process suggests a more structured design approach. This study introduces a novel framework for integrating LLMs into robotic feedback control, with promising practical applicability.