论文标题
查看未来的：学习房间导航的Amodal语义图
Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation
论文作者
论文摘要
我们介绍了一种基于学习的方法，用于使用语义图。我们提出的架构学会了预测自上而下的地区的信念图，这些地区超出了代理商的视野，同时对房屋中的建筑和风格规律进行了建模。首先，我们训练一个模型来生成Amodal语义自上而下的地图，通过学习房屋中的基本建筑模式，指示房间的位置，大小和形状的信念。接下来，我们使用这些地图来预测目标房间中的一点，并训练政策以导航到这一点。我们从经验上证明，通过预测语义图，该模型学习了在房屋中发现的共同相关性，并将其推广到新的环境中。我们还证明，减少房间导航的任务以指向导航进一步提高性能。
We introduce a learning-based approach for room navigation using semantic maps. Our proposed architecture learns to predict top-down belief maps of regions that lie beyond the agent's field of view while modeling architectural and stylistic regularities in houses. First, we train a model to generate amodal semantic top-down maps indicating beliefs of location, size, and shape of rooms by learning the underlying architectural patterns in houses. Next, we use these maps to predict a point that lies in the target room and train a policy to navigate to the point. We empirically demonstrate that by predicting semantic maps, the model learns common correlations found in houses and generalizes to novel environments. We also demonstrate that reducing the task of room navigation to point navigation improves the performance further.
