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MQA: Answering the Question via Robotic Manipulation

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arxiv 2003.04641 v4 pith:DCVGKL3I submitted 2020-03-10 cs.AI cs.CVcs.RO

Yuhong Deng , Di Guo , Xiaofeng Guo , Naifu Zhang , Huaping Liu , Fuchun Sun This is my paper
classification cs.AIcs.CVcs.RO
keywords manipulationquestionmoduleansweringenvironmentrobotactionsanswer
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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.

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