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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| Vydáno v: | IEEE robotics and automation letters Ročník 6; číslo 2; s. 1090 - 1096 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Piscataway
IEEE
01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2377-3766, 2377-3766 |
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| Abstract | 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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| AbstractList | 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. |
| Author | Liu, Bo Xiao, Xuesu Stone, Peter |
| Author_xml | – sequence: 1 givenname: Bo orcidid: 0000-0002-8158-4129 surname: Liu fullname: Liu, Bo email: xiao@cs.utexas.edu organization: Department of Computer Science, The University of Texas at Austin, Austin, TX, United States of America – sequence: 2 givenname: Xuesu orcidid: 0000-0001-5151-2186 surname: Xiao fullname: Xiao, Xuesu email: bliu@cs.utexas.edu organization: Department of Computer Science, The University of Texas at Austin, Austin, TX, United States of America – sequence: 3 givenname: Peter orcidid: 0000-0002-6795-420X surname: Stone fullname: Stone, Peter email: pstone@cs.utexas.edu organization: Department of Computer Science, The University of Texas at Austin, Austin, TX, United States of America |
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| SubjectTerms | 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 |
| Title | A Lifelong Learning Approach to Mobile Robot Navigation |
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