MQA: Answering the Question via Robotic Manipulation
This addresses the problem of enabling robots to answer questions through physical interaction, though it is incremental as it combines existing QA and manipulation methods.
The paper introduces Manipulation Question Answering (MQA), a task where a robot performs manipulation actions to change the environment and answer questions, achieving a 78.3% accuracy on their novel dataset.
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.