The ability to autonomously explore and navigate a physical space is a
fundamental requirement for virtually any mobile autonomous agent, from
household robotic vacuums to autonomous vehicles. Traditional SLAM-based
approaches for exploration and navigation largely focus on leveraging scene
geometry, but fail to model dynamic objects (such as other agents) or semantic
constraints (such as wet floors or doorways). Learning-based RL agents are an
attractive alternative because they can incorporate both semantic and geometric
information, but are notoriously sample inefficient, difficult to generalize to
novel settings, and are difficult to interpret. In this paper, we combine the
best of both worlds with a modular approach that learns a spatial
representation of a scene that is trained to be effective when coupled with
traditional geometric planners. Specifically, we design an agent that learns to
predict a spatial affordance map that elucidates what parts of a scene are
navigable through active self-supervised experience gathering. In contrast to
most simulation environments that assume a static world, we evaluate our
approach in the VizDoom simulator, using large-scale randomly-generated maps
containing a variety of dynamic actors and hazards. We show that learned
affordance maps can be used to augment traditional approaches for both
exploration and navigation, providing significant improvements in performance.

本文提出了一种基于自监督学习的模块化方法，该方法将传统的几何规划与学习空间场景表示相结合，以实现对动态对象和语义约束进行自主探索和导航。在基于 VizDoom 的仿真环境中得到了验证。