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%.
翻译:服务机器人受益于以语义上有意义的方式编码信息,从而能够更稳健地执行任务。 先前的工作显示,多关系嵌入可以编码语义知识图,以促进普遍性和可缩放性,但只能在分批学习范式中进行。 我们介绍了递增语义初始化(ISI),这是一种渐进式学习方法,可以使新的语义概念在嵌入先前学到的语义上类似概念的嵌入中初始化。 我们评估了所埋存的 AI2Thor 和 MatterPort3D 数据集的 SI; 我们的实验显示, 平均而言, ISI 将即时查询性能提高41.4% 。 此外, ISI 方法平均将模型趋同所需的小区数量减少78.2%。