  Incorporating domain-specific priors in search and navigation tasks has shown
promising results in improving generalization and sample complexity over
end-to-end trained policies. In this work, we study how object embeddings that
capture spatial semantic priors can guide search and navigation tasks in a
structured environment. We know that humans can search for an object like a
book, or a plate in an unseen house, based on the spatial semantics of bigger
objects detected. For example, a book is likely to be on a bookshelf or a
table, whereas a plate is likely to be in a cupboard or dishwasher. We propose
a method to incorporate such spatial semantic awareness in robots by leveraging
pre-trained language models and multi-relational knowledge bases as object
embeddings. We demonstrate using these object embeddings to search a query
object in an unseen indoor environment. We measure the performance of these
embeddings in an indoor simulator (AI2Thor). We further evaluate different
pre-trained embedding onSuccess Rate(SR) and success weighted by Path
Length(SPL).
