  Cross-Modal Retrieval (CMR), which retrieves relevant items from one modality
(e.g., audio) given a query in another modality (e.g., visual), has undergone
significant advancements in recent years. This capability is crucial for robots
to integrate and interpret information across diverse sensory inputs. However,
the retrieval space in existing robotic CMR approaches often consists of only
one modality, which limits the robot's performance. In this paper, we propose a
novel CMR model that incorporates three different modalities, i.e., visual,
audio and tactile, for enhanced multi-modal object retrieval, named as VAT-CMR.
In this model, multi-modal representations are first fused to provide a
holistic view of object features. To mitigate the semantic gaps between
representations of different modalities, a dominant modality is then selected
during the classification training phase to improve the distinctiveness of the
representations, so as to improve the retrieval performance. To evaluate our
proposed approach, we conducted a case study and the results demonstrate that
our VAT-CMR model surpasses competing approaches. Further, our proposed
dominant modality selection significantly enhances cross-retrieval accuracy.
