A Lifelong Learning Approach to Mobile Robot Navigation
This addresses the challenge of adaptive and efficient navigation for mobile robots in varying environments, though it appears incremental as it builds on existing lifelong learning concepts.
The paper tackles the problem of mobile robot navigation by proposing a lifelong learning framework that enables a robot to improve its navigation behavior based on its own experience and retain capabilities in previous environments, tested onboard with limited resources.
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.