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

TL;DR

This paper introduces a multimodal classifier model for understanding domestic robot fetching instructions, enhanced with GANs for data augmentation, outperforming previous methods in scene understanding tasks.

Contribution

The paper presents a novel Multimodal Target-source Classifier Model (MTCM) and extends it with GANs to improve instruction understanding in robotic fetching tasks.

Findings

MTCM outperforms state-of-the-art methods on standard datasets.

GAN extension enables effective data augmentation and classification.

The approach accurately infers target and source regions in scenes.

Abstract

In this paper, we address multimodal language understanding for unconstrained fetching instruction in domestic service robots context. A typical fetching instruction such as "Bring me the yellow toy from the white shelf" requires to infer the user intention, that is 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 regionwise classification based on linguistic and visual features. We evaluated our approach that outperformed the state-of-the-art method on a standard data set. In addition, we extended MTCM with Generative Adversarial Nets (MTCM-GAN), and enabled simultaneous data augmentation and classification.