In this letter, we address multimodal language understanding with unconstrained fetching instruction for domestic service robots. A typical fetching instruction such as "Bring me the yellow toy from the white shelf" requires to infer the user intention, i.e., what object (target) to fetch and from where (source). To solve the task, we propose a multimodal target-source classifier model (MTCM), which predicts the region-wise likelihood of target and source candidates in the scene. Unlike other methods, MTCM can handle region-wise classification based on linguistic and visual features. We evaluated our approach that outperformed the stateof-the-art method on a standard dataset. We also extended MTCM with generative adversarial nets, and enabled simultaneous data augmentation and classification.
In our previous work REF , we proposed to use both the target and source candidates to predict the likely target in a supervised manner.
Understanding Natural Language Instructions for Fetching Daily Objects Using GAN-Based Multimodal Target–Source Classification
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters
Computer Science Engineering