MAER-Nav: Bidirectional Motion Learning Through Mirror-Augmented Experience Replay for Robot Navigation
On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 33 references that are in the map. First posted on arXiv 31 Mar 2025.
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- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2017Hindsight Experience Replaycited by 1,136
- 2015Prioritized Experience Replaycited by 1,099
- 2016Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learningcited by 366
- 2021Core Challenges of Social Robot Navigation: A Surveycited by 106
- 2023DRL-VO: Learning to Navigate Through Crowded Dynamic Scenes Using Velocity Obstaclescited by 37
- 2021Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systemscited by 15
- 2021Crowd-Aware Robot Navigation for Pedestrians with Multiple Collision Avoidance Strategies via Map-based Deep Reinforcement Learningcited by 12
- 2021IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learningcited by 10
- 2018The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual Navigationcited by 7
- 2019Reinforcement Learning-based Visual Navigation with Information-Theoretic Regularizationcited by 7
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- 2025X-Nav: Learning End-to-End Cross-Embodiment Navigation for Mobile Robots
- 2026NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation
- 2024Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot
- 2017One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay
- 2025NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance
- 2024Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
- 2018Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrations
- 2020Learning Navigation Costs from Demonstration in Partially Observable Environments