Embodied Question Answering Multi-Step Ahead Image Prediction with Mixture of Experts for Embodied Question Answering

Abstract

In this study, we proposed a subtask that combines multiple scales of visual field prediction and investigated its effectiveness for Embodied Question Answering (EQA). In EQA, it is desirable to be able to automatically select a prediction scale according to the situation, because the path to the target object depends on the instructions given. However, previous studies have only examined subtask learning with a limited prediction scale and target. We propose a mixture of experts model in which multiple expert networks predict future images of different time steps, and a higher-level gating network estimates the distribution of each expert's output. By sequentially adjusting the output of the expert network, the proposed method enables robot navigation considering multiple prediction scales. Comparison experiments on the EQA MP3D dataset show that the proposed method improves the model's prediction accuracy regardless of the distance to the target.