  Training robots to navigate diverse environments is a challenging problem as
it involves the confluence of several different perception tasks such as
mapping and localization, followed by optimal path-planning and control.
Recently released photo-realistic simulators such as Habitat allow for the
training of networks that output control actions directly from perception:
agents use Deep Reinforcement Learning (DRL) to regress directly from the
camera image to a control output in an end-to-end fashion. This is
data-inefficient and can take several days to train on a GPU. Our paper tries
to overcome this problem by separating the training of the perception and
control neural nets and increasing the path complexity gradually using a
curriculum approach. Specifically, a pre-trained twin Variational AutoEncoder
(VAE) is used to compress RGBD (RGB & depth) sensing from an environment into a
latent embedding, which is then used to train a DRL-based control policy. A*, a
traditional path-planner is used as a guide for the policy and the distance
between start and target locations is incrementally increased along the A*
route, as training progresses. We demonstrate the efficacy of the proposed
approach, both in terms of increased performance and decreased training times
for the PointNav task in the Habitat simulation environment. This strategy of
improving the training of direct-perception based DRL navigation policies is
expected to hasten the deployment of robots of particular interest to industry
such as co-bots on the factory floor and last-mile delivery robots.
