  This paper presents a novel approach to generating behavior trees for robots
using lightweight large language models (LLMs) with a maximum of 7 billion
parameters. The study demonstrates that it is possible to achieve satisfying
results with compact LLMs when fine-tuned on a specific dataset. The key
contributions of this research include the creation of a fine-tuning dataset
based on existing behavior trees using GPT-3.5 and a comprehensive comparison
of multiple LLMs (namely llama2, llama-chat, and code-llama) across nine
distinct tasks. To be thorough, we evaluated the generated behavior trees using
static syntactical analysis, a validation system, a simulated environment, and
a real robot. Furthermore, this work opens the possibility of deploying such
solutions directly on the robot, enhancing its practical applicability.
Findings from this study demonstrate the potential of LLMs with a limited
number of parameters in generating effective and efficient robot behaviors.
