Active Visual Navigation in an Unexplored Environment

该项目将开发一种新的机器人导航方法，其中目标可以在比以前更高的抽象层次上指定。这将通过使用深度学习来对场景布局进行明智的预测，并作为主动观察者进行导航，其中预测通知动作。结果将是机器人代理能够在以前看不见的环境中有效和高效地导航和操作，并且能够用更像人类的指令控制这些代理。这种能力是理想的，在某些情况下是必不可少的，在各种重要的应用领域，包括自动化仓储和自主车辆的高级控制的自主机器人。

This project will develop a new method for robotic navigation in which goals can be specified at a much higher level of abstraction than has previously been possible. This will be achieved using deep learning to make informed predictions about a scene layout, and navigating as an active observer in which the predictions informs actions. The outcome will be robotic agents capable of effective and efficient navigation and operation in previously unseen environments, and the ability to control such agents with more human-like instructions. Such capabilities are desirable, and in some cases essential, for autonomous robots in a variety of important application areas including automated warehousing and high-level control of autonomous vehicles.