A Lifelong Learning Approach to Mobile Robot Navigation

• Published in: IEEE robotics and automation letters Vol. 6; no. 2; pp. 1090 - 1096
• Main Authors: Liu, Bo, Xiao, Xuesu, Stone, Peter
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: autonomous vehicle navigation; Computation; imitation learning; Lifelong learning; machine learning for robot control; Mobile robots; Motion and path planning; Navigation; Optimization; Robot sensing systems; Robots; sensorimotor learning; Task analysis; Training; Tuning
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2021.3056373

This letter 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.