  Adaptability is central to autonomy. Intuitively, for high-dimensional
learning problems such as navigating based on vision, internal models with
higher complexity allow to accurately encode the information available.
However, most learning methods rely on models with a fixed structure and
complexity. In this paper, we present a self-supervised framework for robots to
learn to navigate, without any prior knowledge of the environment, by
incrementally building the structure of a deep network as new data becomes
available. Our framework captures images from a monocular camera and self
labels the images to continuously train and predict actions from a
computationally efficient adaptive deep architecture based on Autoencoders
(AE), in a self-supervised fashion. The deep architecture, named Reinforced
Adaptive Denoising Autoencoders (RA-DAE), uses reinforcement learning to
dynamically change the network structure by adding or removing neurons.
Experiments were conducted in simulation and real-world indoor and outdoor
environments to assess the potential of self-supervised navigation. RA-DAE
demonstrates better performance than equivalent non-adaptive deep learning
alternatives and can continue to expand its knowledge, trading-off past and
present information.
