  How can a robot navigate successfully in rich and diverse environments,
indoors or outdoors, along office corridors or trails on the grassland, on the
flat ground or the staircase? To this end, this work aims to address three
challenges: (i) complex visual observations, (ii) partial observability of
local visual sensing, and (iii) multimodal robot behaviors conditioned on both
the local environment and the global navigation objective. We propose to train
a neural network (NN) controller for local navigation via imitation learning.
To tackle complex visual observations, we extract multi-scale spatial
representations through CNNs. To tackle partial observability, we aggregate
multi-scale spatial information over time and encode it in LSTMs. To learn
multimodal behaviors, we use a separate memory module for each behavior mode.
Importantly, we integrate the multiple neural network modules into a unified
controller that achieves robust performance for visual navigation in complex,
partially observable environments. We implemented the controller on the
quadrupedal Spot robot and evaluated it on three challenging tasks: adversarial
pedestrian avoidance, blind-spot obstacle avoidance, and elevator riding. The
experiments show that the proposed NN architecture significantly improves
navigation performance.
