Indoor Visual Navigation Based on Deep Reinforcement Learning with Neural Ordinary Differential Equations

Recently, Deep Reinforcement Learning (DRL) has gained attention as a promising approach to tackle the challenging problem of mobile robot navigation. This study proposes reinforcement learning utilizing Neural Ordinary Differential Equations (NODEs), which offer effective training and memory capacities, and applies it to model-free point-to-point navigation task. Through the use of NODEs, we achieved improvements in navigation performance as well as enhancements in resource optimization and adaptation. Extensive simulation studies were conducted using real-world indoor scenes to validate our approach. Results effectively demonstrated the effectiveness of our proposed NODEs-based methodology in enhancing navigation performance compared to traditional ResNet and CNN architectures. Furthermore, curriculum learning strategies were integrated into our study to enable the agent to learn through progressively more complex navigation scenarios. The results obtained indicate that this approach facilitates faster and more robust reinforcement learning.