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.

翻译：基于深度强化学习（DRL）的导航方法已在移动机器人领域展现出有前景的结果，但在受限空间中存在动作灵活性不足的问题。传统的DRL方法主要学习前向运动策略，导致机器人在复杂环境中容易陷入困境，而这些环境往往需要后向机动操作才能恢复。本文提出了MAER-Nav（用于机器人导航的镜像增强经验回放），这是一个新颖的框架，无需显式的失败驱动事后经验回放或奖励函数修改，即可实现双向运动学习。我们的方法将镜像增强经验回放机制与课程学习相结合，从成功轨迹中生成合成的后向导航经验。仿真和真实环境中的实验结果表明，MAER-Nav在保持强大前向导航能力的同时，显著优于现有最先进方法。该框架有效弥合了传统规划方法在全面利用动作空间与基于学习的方法在环境适应性之间的差距，能够在传统DRL方法持续失败的场景中实现鲁棒导航。