  This paper presents a self-improving lifelong learning framework for a mobile
robot navigating in different environments. Classical static navigation methods
require environment-specific in-situ system adjustment, e.g. from human
experts, or may repeat their mistakes regardless of how many times they have
navigated in the same environment. Having the potential to improve with
experience, learning-based navigation is highly dependent on access to training
resources, e.g. sufficient memory and fast computation, and is prone to
forgetting previously learned capability, especially when facing different
environments. In this work, we propose Lifelong Learning for Navigation (LLfN)
which (1) improves a mobile robot's navigation behavior purely based on its own
experience, and (2) retains the robot's capability to navigate in previous
environments after learning in new ones. LLfN is implemented and tested
entirely onboard a physical robot with a limited memory and computation budget.
