  This paper presents an improved system based on our prior work, designed to
create explanations for autonomous robot actions during Human-Robot Interaction
(HRI). Previously, we developed a system that used Large Language Models (LLMs)
to interpret logs and produce natural language explanations. In this study, we
expand our approach by incorporating Vision-Language Models (VLMs), enabling
the system to analyze textual logs with the added context of visual input. This
method allows for generating explanations that combine data from the robot's
logs and the images it captures. We tested this enhanced system on a basic
navigation task where the robot needs to avoid a human obstacle. The findings
from this preliminary study indicate that adding visual interpretation improves
our system's explanations by precisely identifying obstacles and increasing the
accuracy of the explanations provided.
