  Building robots capable of interacting with humans through natural language
in the visual world presents a significant challenge in the field of robotics.
To overcome this challenge, Embodied Question Answering (EQA) has been proposed
as a benchmark task to measure the ability to identify an object navigating
through a previously unseen environment in response to human-posed questions.
Although some methods have been proposed, their evaluations have been limited
to simulations, without experiments in real-world scenarios. Furthermore, all
of these methods are constrained by a limited vocabulary for
question-and-answer interactions, making them unsuitable for practical
applications. In this work, we propose a map-based modular EQA method that
enables real robots to navigate unknown environments through frontier-based map
creation and address unknown QA pairs using foundation models that support open
vocabulary. Unlike the questions of the previous EQA dataset on Matterport 3D
(MP3D), questions in our real-world experiments contain various question
formats and vocabularies not included in the training data. We conduct
comprehensive experiments on virtual environments (MP3D-EQA) and two real-world
house environments and demonstrate that our method can perform EQA even in the
real world.
