让机器人学会前后自由移动，解决复杂空间被困难题。
MAER-Nav: Bidirectional Motion Learning Through Mirror-Augmented Experience Replay for Robot Navigation
- 通过镜像回放生成反向运动经验，无需额外奖励或失败重播
- 在仿真与真实环境均显著超越现有方法，成功率大幅提升
- 适合需要灵活避障的移动机器人导航场景
基于深度强化学习的导航方法在移动机器人中表现优异，但在狭窄空间中动作灵活性受限。传统方法主要学习前向运动策略，导致机器人在需倒车恢复的复杂环境中易被困。本文提出MAER-Nav（镜像增强经验回放的机器人导航框架），通过镜像增强经验回放机制结合课程学习，从成功轨迹中合成反向导航经验，实现双向运动学习，无需显式失败驱动的回放或奖励函数修改。实验结果表明，该框架在仿真与真实环境中的表现显著优于当前最优方法，同时保持良好的前向导航能力。其有效弥合了传统规划方法对动作空间的全面利用与学习型方法环境适应性之间的差距，使机器人在常规DRL方法持续失效的场景下仍能稳健导航。
原文摘要 · Abstract (English)
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.
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