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In this paper, we address multimodal language understanding for unconstrainedfetching instruction in domestic service robots context. A typical fetchinginstruction such as "Bring me the yellow toy from the white shelf" requires toinfer the user intention, that is what object (target) to fetch and from where(source). To solve the task, we propose a Multimodal Target-source ClassifierModel (MTCM), which predicts the region-wise likelihood of target and sourcecandidates in the scene. Unlike other methods, MTCM can handle regionwiseclassification based on linguistic and visual features. We evaluated ourapproach that outperformed the state-of-the-art method on a standard data set.In addition, we extended MTCM with Generative Adversarial Nets (MTCM-GAN), andenabled simultaneous data augmentation and classification.

Understanding Natural Language Instructions for Fetching Daily Objects Using GAN-Based Multimodal Target-Source Classification