MQA: Answering the Question via Robotic Manipulation
Abstract
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.
Keywords
Cite
@article{arxiv.2003.04641,
title = {MQA: Answering the Question via Robotic Manipulation},
author = {Yuhong Deng and Di Guo and Xiaofeng Guo and Naifu Zhang and Huaping Liu and Fuchun Sun},
journal= {arXiv preprint arXiv:2003.04641},
year = {2023}
}
Comments
has been accepted by Robotics: Science and Systems 2021