A Lifelong Learning Approach to Mobile Robot Navigation

Understand

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.