Memory-based Exploration-value Evaluation Model for Visual Navigation
Yongquan Feng, Liyang Xu, Minglong Li, Ruochun Jin, Da Huang, Shaowu Yang, Wenjing Yang
Abstract
We propose a hierarchical visual navigation solution, called Memory-based Exploration-value Evaluation Model (MEEM), to improve the agent's navigation performance. MEEM employs a hierarchical policy to tackle the challenge of sparse rewards, holds an episodic memory to store the historical information of the agent, and applies an Exploration-value Evaluation Model to calculate an exploration-value for action planning at each location in the observable area. We experimentally verify MEEM by navigation performance comparison on two datasets including the grid-map dataset and the 3D scenes Gibson dataset, where our approach achieves state-of-the-art performance on both. Specifically, the overall success rate of MEEM is 95% on the grid-map dataset while the best competitor reaches 68% only. As for the Gibson dataset, the success rate of ours and the best competitor SemExp are 69.8% and 54.4%, respectively. Ablation analysis on the tile-map dataset indicates that all three components of MEEM have positive effects.
BibTeX
@inproceedings{icra2023_memorybasedexplo,
title = {Memory-based Exploration-value Evaluation Model for Visual Navigation},
author = {Yongquan Feng and Liyang Xu and Minglong Li and Ruochun Jin and Da Huang and Shaowu Yang and Wenjing Yang},
booktitle = {ICRA 2023},
year = {2023}
}