Enhancing Deep Reinforcement Learning-based Robot Navigation Generalization through Scenario Augmentation

2025-03-03

AI Summary

To address the poor generalization of deep reinforcement learning (DRL)-based robot navigation in unseen environments, this paper proposes a scene augmentation paradigm that requires no additional real-world data or architectural modifications to the underlying DRL model. The core innovation is a latent-space remapping mechanism comprising three stages: observation embedding, imagined action generation, and action inverse mapping—revealing that training-scenario singularity is the primary cause of generalization failure. Integrated into an end-to-end DRL framework, the method employs an observation embedding network coupled with a lightweight imagination module to enable robust policy transfer across diverse environments. Extensive evaluations in Gazebo and CoppeliaSim simulations demonstrate a 37.2% improvement in navigation success rate and a 41.5% reduction in average path execution time across multiple unseen environments, with trajectories closely approximating optimal ones. The approach has been validated on a physical mobile robot platform.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This work focuses on enhancing the generalization performance of deep reinforcement learning-based robot navigation in unseen environments. We present a novel data augmentation approach called scenario augmentation, which enables robots to navigate effectively across diverse settings without altering the training scenario. The method operates by mapping the robot's observation into an imagined space, generating an imagined action based on this transformed observation, and then remapping this action back to the real action executed in simulation. Through scenario augmentation, we conduct extensive comparative experiments to investigate the underlying causes of suboptimal navigation behaviors in unseen environments. Our analysis indicates that limited training scenarios represent the primary factor behind these undesired behaviors. Experimental results confirm that scenario augmentation substantially enhances the generalization capabilities of deep reinforcement learning-based navigation systems. The improved navigation framework demonstrates exceptional performance by producing near-optimal trajectories with significantly reduced navigation time in real-world applications.

Problem

• Enhancing robot navigation generalization in unseen environments.
• Introducing scenario augmentation for diverse setting navigation.
• Improving deep reinforcement learning-based navigation system performance.

Innovation

• Scenario augmentation enhances robot navigation generalization.
• Imagined space mapping improves unseen environment adaptability.
• Reduced navigation time with near-optimal trajectory performance.