We, as humans, can impeccably navigate to localise a target object, even in an unseen environment. We argue that this impressive ability is largely due to incorporation of \emph{prior knowledge} (or experience) and \emph{visual cues}--that current visual navigation approaches lack. In this paper, we propose to use externally learned prior knowledge of object relations, which is integrated to our model via constructing a neural graph. To combine appropriate assessment of the states and the prior (knowledge), we propose to decompose the value function in the actor-critic reinforcement learning algorithm and incorporate the prior in the critic in a novel way that reduces the model complexity and improves model generalisation. Our approach outperforms the current state-of-the-art in AI2THOR visual navigation dataset.

中文翻译：

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利用视觉导航的先验知识：提炼和适应

作为人类，即使在看不见的环境中，我们也可以无可挑剔地导航以定位目标对象。我们认为这种令人印象深刻的能力主要是由于\emph{先验知识}（或经验）和\emph{视觉线索}的结合——目前的视觉导航方法缺乏。在本文中，我们建议使用外部学习的对象关系先验知识，通过构建神经图将其集成到我们的模型中。为了结合对状态的适当评估和先验（知识），我们建议分解 actor-critic 强化学习算法中的价值函数，并以一种降低模型复杂性并提高模型泛化的新方式将先验合并到 critic 中。我们的方法在 AI2THOR 视觉导航数据集中优于当前最先进的方法。