  In this paper, we propose a novel task, Manipulation Question Answering
(MQA), where the robot performs manipulation actions to change the environment
in order to answer a given question. To solve this problem, a framework
consisting of a QA module and a manipulation module is proposed. For the QA
module, we adopt the method for the Visual Question Answering (VQA) task. For
the manipulation module, a Deep Q Network (DQN) model is designed to generate
manipulation actions for the robot to interact with the environment. We
consider the situation where the robot continuously manipulating objects inside
a bin until the answer to the question is found. Besides, a novel dataset that
contains a variety of object models, scenarios and corresponding
question-answer pairs is established in a simulation environment. Extensive
experiments have been conducted to validate the effectiveness of the proposed
framework.
