Reinforcement Learning Based Navigation with Semantic Knowledge of Indoor Environments

Recent years have been witnessing a huge step of artificial intelligence towards being applied in autonomous robots. To build intelligent robots navigating in indoor environment, many research focus on deep reinforcement learning which help robot learn and plan by themselves. Different network architectures are proposed for training agents to navigate and find targeted objects in both real and simulated environments. Despite promising results, one key challenge remaining is that the agent has to perform well in unseen environments and objects. To solve this generalization problem, this work proposes a method using prior knowledge graph capturing relationships between target objects. Experiments on simulated environments show that not only the proposed method enhances the learning process but also significantly improves agents generalization. When compared to similar methods, proposed method has a competitive and even better performance while bringing computational advantages.