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

TL;DR

This paper introduces PEMR, a novel route planning framework for embodied navigation that mimics human foresight and memory, significantly improving performance in unknown environments.

Contribution

The paper proposes PEMR, a new navigation framework with path estimation and memory recall modules, enhancing exploration in unfamiliar scenes for embodied agents.

Findings

PEMR outperforms existing navigation algorithms on EmbodiedQA tasks.

The framework effectively estimates feasible paths and leverages past experience.

Experimental results demonstrate improved accuracy in navigation and question answering.

Abstract

An embodied task such as embodied question answering (EmbodiedQA), requires an agent to explore the environment and collect clues to answer a given question that related with specific objects in the scene. The solution of such task usually includes two stages, a navigator and a visual Q&A module. In this paper, we focus on the navigation and solve the problem of existing navigation algorithms lacking experience and common sense, which essentially results in a failure finding target when robot is spawn in unknown environments. Inspired by the human ability to think twice before moving and conceive several feasible paths to seek a goal in unfamiliar scenes, we present a route planning method named Path Estimation and Memory Recalling (PEMR) framework. PEMR includes a "looking ahead" process, \textit{i.e.} a visual feature extractor module that estimates feasible paths for gathering 3D navigational information, which is mimicking the human sense of direction. PEMR contains another process ``looking behind'' process that is a memory recall mechanism aims at fully leveraging past experience collected by the feature extractor. Last but not the least, 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.