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arXiv · 2108.00159

Learning Embeddings that Capture Spatial Semantics for Indoor Navigation

Vidhi Jain·Prakhar Agarwal·Shishir Patil·Katia Sycara
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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 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).

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Vidhi Jain, Prakhar Agarwal, Shishir Patil, Katia Sycara. 2021-07-31. Learning Embeddings that Capture Spatial Semantics for Indoor Navigation. https://arxiv.org/abs/2108.00159

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