Feudal Networks for Visual Navigation
Faith Johnson Bryan Bo Cao Kristin Dana Shubham Jain Ashwin Ashok
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.