Visual navigation follows the intuition that humans can navigate without
detailed maps. A common approach is interactive exploration while building a
topological graph with images at nodes that can be used for planning. Recent
variations learn from passive videos and can navigate using complex social and
semantic cues. However, a significant number of training videos are needed,
large graphs are utilized, and scenes are not unseen since odometry is
utilized. We introduce a new approach to visual navigation using feudal
learning, which employs a hierarchical structure consisting of a worker agent,
a mid-level manager, and a high-level manager. Key to the feudal learning
paradigm, agents at each level see a different aspect of the task and operate
at different spatial and temporal scales. Two unique modules are developed in
this framework. For the high- level manager, we learn a memory proxy map in a
self supervised manner to record prior observations in a learned latent space
and avoid the use of graphs and odometry. For the mid-level manager, we develop
a waypoint network that outputs intermediate subgoals imitating human waypoint
selection during local navigation. This waypoint network is pre-trained using a
new, small set of teleoperation videos that we make publicly available, with
training environments different from testing environments. The resulting feudal
navigation network achieves near SOTA performance, while providing a novel
no-RL, no-graph, no-odometry, no-metric map approach to the image goal
navigation task.

通过使用封建学习的新方法，我们构建了一个由工人代理、中层经理和高层经理组成的层次结构，其中，高层经理使用自我监督的方式学习了一个内在空间的记忆代理图，中层经理经由一种新的代理网络进行训练，以模仿人类在局部导航过程中选择中间目标的行为，从而实现了接近最强系统的表现，并提供了一种新的、无强化学习、无图表、无测距法、无度量图的图像目标导航方法。