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Service robots benefit from encoding information in semantically meaningfulways to enable more robust task execution. Prior work has shownmulti-relational embeddings can encode semantic knowledge graphs to promotegeneralizability and scalability, but only within a batched learning paradigm.We present Incremental Semantic Initialization (ISI), an incremental learningapproach that enables novel semantic concepts to be initialized in theembedding in relation to previously learned embeddings of semantically similarconcepts. We evaluate ISI on mined AI2Thor and MatterPort3D datasets; ourexperiments show that on average ISI improves immediate query performance by41.4%. Additionally, ISI methods on average reduced the number of epochsrequired to approach model convergence by 78.2%.

Leveraging Semantics for Incremental Learning in Multi-Relational Embeddings