  Robots that assist in daily life are required to locate specific instances of
objects that match the user's desired object in the environment. This task is
known as Instance-Specific Image Goal Navigation (InstanceImageNav), which
requires a model capable of distinguishing between different instances within
the same class. One significant challenge in robotics is that when a robot
observes the same object from various 3D viewpoints, its appearance may differ
greatly, making it difficult to recognize and locate the object accurately. In
this study, we introduce a method, SimView, that leverages multi-view images
based on a 3D semantic map of the environment and self-supervised learning by
SimSiam to train an instance identification model on-site. The effectiveness of
our approach is validated using a photorealistic simulator, Habitat Matterport
3D, created by scanning real home environments. Our results demonstrate a
1.7-fold improvement in task accuracy compared to CLIP, which is pre-trained
multimodal contrastive learning for object search. This improvement highlights
the benefits of our proposed fine-tuning method in enhancing the performance of
assistive robots in InstanceImageNav tasks. The project website is
https://emergentsystemlabstudent.github.io/MultiViewRetrieve/.
