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).

中文翻译：

---

为室内导航捕捉空间语义的学习嵌入

在搜索和导航任务中结合特定领域的先验在提高泛化和样本复杂性方面取得了可喜的成果，而不是端到端训练有素的策略。在这项工作中，我们研究了捕获空间语义先验的对象嵌入如何指导结构化环境中的搜索和导航任务。我们知道，人类可以根据检测到的较大物体的空间语义来搜索诸如书本或看不见的房子中的盘子之类的物体。例如，一本书可能在书架或桌子上，而盘子可能在橱柜或洗碗机中。我们提出了一种方法，通过利用预先训练的语言模型和多关系知识库作为对象嵌入，将这种空间语义意识纳入机器人中。我们演示了使用这些对象嵌入在看不见的室内环境中搜索查询对象。我们在室内模拟器 (AI2Thor) 中测量这些嵌入的性能。我们进一步评估了不同的预训练嵌入成功率（SR）和由路径长度（SPL）加权的成功。