MAER-Nav: 鏡像拡張経験リプレイによるロボットナビゲーションの双方向運動学習
MAER-Nav: Bidirectional Motion Learning Through Mirror-Augmented Experience Replay for Robot Navigation
成功した前進軌跡を鏡像反転して後退経験を合成する経験リプレイとカリキュラム学習を組み合わせ、明示的な失敗駆動型リプレイや報酬設計の変更なしに双方向運動を学習するナビゲーション手法を提案した。
著者: Shanze Wang, Mingao Tan, Zhibo Yang, Biao Huang, Xiaoyu Shen, Hailong Huang, Wei Zhang
分類: cs.RO
原文アブストラクト
Deep Reinforcement Learning (DRL) based navigation methods have demonstrated promising results for mobile robots, but suffer from limited action flexibility in confined spaces. Conventional DRL approaches predominantly learn forward-motion policies, causing robots to become trapped in complex environments where backward maneuvers are necessary for recovery. This paper presents MAER-Nav (Mirror-Augmented Experience Replay for Robot Navigation), a novel framework that enables bidirectional motion learning without requiring explicit failure-driven hindsight experience replay or reward function modifications. Our approach integrates a mirror-augmented experience replay mechanism with curriculum learning to generate synthetic backward navigation experiences from successful trajectories. Experimental results in both simulation and real-world environments demonstrate that MAER-Nav significantly outperforms state-of-the-art methods while maintaining strong forward navigation capabilities. The framework effectively bridges the gap between the comprehensive action space utilization of traditional planning methods and the environmental adaptability of learning-based approaches, enabling robust navigation in scenarios where conventional DRL methods consistently fail.
