A Lifelong Learning Approach to Mobile Robot Navigation

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 t...

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Veröffentlicht in:IEEE robotics and automation letters Jg. 6; H. 2; S. 1090 - 1096
Hauptverfasser: Liu, Bo, Xiao, Xuesu, Stone, Peter
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3056373