  Domestic service robots (DSRs) that support people in everyday environments
have been widely investigated. However, their ability to predict and describe
future risks resulting from their own actions remains insufficient. In this
study, we focus on the linguistic explainability of DSRs. Most existing methods
do not explicitly model the region of possible collisions; thus, they do not
properly generate descriptions of these regions. In this paper, we propose the
Nearest Neighbor Future Captioning Model that introduces the Nearest Neighbor
Language Model for future captioning of possible collisions, which enhances the
model output with a nearest neighbors retrieval mechanism. Furthermore, we
introduce the Collision Attention Module that attends regions of possible
collisions, which enables our model to generate descriptions that adequately
reflect the objects associated with possible collisions. To validate our
method, we constructed a new dataset containing samples of collisions that can
occur when a DSR places an object in a simulation environment. The experimental
results demonstrated that our method outperformed baseline methods, based on
the standard metrics. In particular, on CIDEr-D, the baseline method obtained
25.09 points, whereas our method obtained 33.08 points.
