Citation

Tan, Chi Jie and Hayashi, Eiji and Mowshowitz, Abbe and Lim, Way Soong (2026) Hybrid LLM-Genetic Programming: Supervising and Generating Diverse Behavior Trees for Autonomous Robot Evolution. Robotics, 15 (5). p. 98. ISSN 2218-6581

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Abstract

Genetic Programming (GP) for evolving Behavior Trees (BTs) in autonomous robots often suffer from premature convergence, even when adaptive mutation mechanisms are employed. This paper proposes a novel hybrid framework that integrates Large Language Model (LLM) supervision into GP, in which the LLM performs holistic population analysis, adaptively regulates mutation rates, and generates targeted BTs to proactively address behavioral gaps in the evolving population. Unlike conventional evolutionary operators, the LLM introduces high-level semantic guidance by seeding underrepresented behavioral archetypes, thereby complementing stochastic genetic variation with structured exploration. The proposed method is evaluated in a Unity-based multi-task robotic simulation environment. Experimental results show that the hybrid approach significantly outperforms baseline GP with standard adaptive mutation, achieving a 71.7% faster emergence of Complete Robots, a 65.2% faster emergence of Excellent Robots, and a 28% increase in behavioral diversity. Notably, the two systems exhibit opposite mutation dynamics: the LLM-guided system progressively reduces mutation rates to promote exploitation, whereas the baseline maintains a high mutation rate. In addition, the LLM generates approximately 40 targeted BTs per run, proactively seeding the population with underrepresented behavioral archetypes. These performance gains are obtained with only a 13% computational overhead.

Item Type:	Article
Uncontrolled Keywords:	Genetic programming, behavior trees
Subjects:	T Technology > TJ Mechanical Engineering and Machinery > TJ212-225 Control engineering systems. Automatic machinery (General)
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Jun 2026 08:36
Last Modified:	09 Jul 2026 07:59
URII:	http://shdl.mmu.edu.my/id/eprint/16162

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