Learning Embeddings that Capture Spatial Semantics for Indoor Navigation
This work addresses the challenge of sample complexity and generalization in robotics navigation for indoor settings, though it appears incremental by building on existing methods like pre-trained language models.
The paper tackles the problem of improving object search and navigation in unseen indoor environments by incorporating spatial semantic priors into object embeddings, resulting in performance measured by Success Rate (SR) and Success weighted by Path Length (SPL) in the AI2Thor simulator.
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).