  Service robots benefit from encoding information in semantically meaningful
ways to enable more robust task execution. Prior work has shown
multi-relational embeddings can encode semantic knowledge graphs to promote
generalizability and scalability, but only within a batched learning paradigm.
We present Incremental Semantic Initialization (ISI), an incremental learning
approach that enables novel semantic concepts to be initialized in the
embedding in relation to previously learned embeddings of semantically similar
concepts. We evaluate ISI on mined AI2Thor and MatterPort3D datasets; our
experiments show that on average ISI improves immediate query performance by
41.4%. Additionally, ISI methods on average reduced the number of epochs
required to approach model convergence by 78.2%.
