Object Instance Retrieval in Assistive Robotics: Leveraging Fine-Tuned SimSiam with Multi-View Images Based on 3D Semantic Map
<|reference_start|>Object Instance Retrieval in Assistive Robotics: Leveraging Fine-Tuned SimSiam with Multi-View Images Based on 3D Semantic Map: Robots that assist humans in their daily lives should be able to locate specific instances of objects in an environment that match a user's desired objects. This task is known as instance-specific image goal navigation (InstanceImageNav), which requires a model that can distinguish different instances of an object within the same class. A significant challenge in robotics is that when a robot observes the same object from various 3D viewpoints, its appearance may differ significantly, making it difficult to recognize and locate accurately. In this paper, we introduce a method called SimView, which leverages multi-view images based on a 3D semantic map of an environment and self-supervised learning using SimSiam to train an instance-identification model on-site. The effectiveness of our approach was validated using a photorealistic simulator, Habitat Matterport 3D, created by scanning actual home environments. Our results demonstrate a 1.7-fold improvement in task accuracy compared with contrastive language-image pre-training (CLIP), a pre-trained multimodal contrastive learning method for object searching. 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/.<|reference_end|>
@article{sakaguchi2024object,
    title={Object Instance Retrieval in Assistive Robotics: Leveraging Fine-Tuned
  SimSiam with Multi-View Images Based on 3D Semantic Map},
    author={Taichi Sakaguchi, Akira Taniguchi, Yoshinobu Hagiwara, Lotfi El Hafi,
  Shoichi Hasegawa, Tadahiro Taniguchi},
    journal={arXiv preprint arXiv:2404.09647},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09647},
    primaryClass={cs.RO}
}