Learning Embeddings that Capture Spatial Semantics for Indoor Navigation

Abstract

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 task 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 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 on Success Rate (SR) and Success weighted by Path Length (SPL).