# MQA: Answering the Question via Robotic Manipulation

Source: [MQA: Answering the Question via Robotic Manipulation](<https://www.sota2.com/research/paper/68fa6e971e209131cea40f62>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:01:51.507Z

Authors: Yuhong Deng, Di Guo, Xiaofeng Guo, Naifu Zhang, Huaping Liu, Fuchun Sun

Published: 2020-03-10T11:30:09.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2003.04641>)

[PDF](<https://arxiv.org/pdf/2003.04641.pdf>)