Memory Proxy Maps for Visual Navigation

Faith M Johnson, Bryan Bo Cao, Ashwin Ashok, Shubham Jain, Kristin Dana

Keywords: visual navigation, representation learning, hierarchical networks

TL;DR: a hierarchical approach to the visual navigation task that doesn't use odometric, metric maps, graphs, rl, or learning in simulators

Abstract:

Visual navigation takes inspiration from humans, who navigate in previously unseen environments using vision without detailed environment maps. Inspired by this, we introduce a novel no-RL, no-graph, no-odometry approach to visual navigation using feudal learning to build a three tiered agent. Key to our approach is a memory proxy map (MPM), an intermediate representation of the environment
learned in a self-supervised manner by the high-level manager agent that serves as a simplified memory, approximating what the agent has seen. We demonstrate that recording observations in this learned latent space is an effective and efficient memory proxy that can remove the need for graphs and odometry in visual navigation tasks. For the mid-level manager agent, we develop a waypoint network
(WayNet) that outputs intermediate subgoals, or waypoints, imitating human waypoint selection during local navigation. For the low-level worker agent, we learn a classifier over a discrete action space that avoids local obstacles and moves the agent towards the WayNet waypoint. The resulting feudal navigation network offers a novel approach with no RL, no graph, no odometry, and no metric map; all while achieving SOTA results on the image goal navigation task.

Primary Area: applications to robotics, autonomy, planning

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Submission Number: 5038