Explore before Moving: A Feasible Path Estimation and Memory Recalling Framework for Embodied Navigation

Yang Wu, Shirui Feng, Guanbin Li, Liang Lin

Abstract: In this paper, we focus on solving the navigation problem of embodied question answering (EmbodiedQA), where the lack of experience and common sense information essentially result in a failure finding target when the robot is spawn in unknown environments. We present a route planning method named Path Estimation and Memory Recalling (PEMR) framework. PEMR includes a “looking ahead” process, i.e. a visual feature extractor module that estimates feasible paths for gathering 3D navigational information; another process “looking behind” process that is a memory recalling mechanism aims at fully leveraging past experience collected by the feature extractor. To encourage the navigator to learn more accurate prior expert experience, we improve the original benchmark dataset and provide a family of evaluation metrics for diagnosing both navigation and question answering modules. We show strong experimental results of PEMR on the EmbodiedQA navigation task.