Abstract
In this paper, a novel online learning navigation algorithm is proposed to incorporate negative data generated from failure in an online manner. While existing methods require additional knowledge about what to do at failed situations, the proposed method alleviates this by utilizing failures as a clue of what not to do without requiring additional knowledge of what to do. By combining the benefits of leveraged Gaussian processes and sparse online Gaussian processes, we proposed an online learning framework for navigation and its update rule which instantly learns which actions to avoid from the failures while navigating. Our navigation method is successfully validated on a static planar world and dynamic worlds on both simulation and real-world dataset.
Original language | English |
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Title of host publication | 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 868-873 |
Number of pages | 6 |
ISBN (Electronic) | 9781509030552 |
DOIs | |
Publication status | Published - 2017 Jul 25 |
Externally published | Yes |
Event | 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017 - Jeju, Korea, Republic of Duration: 2017 Jun 28 → 2017 Jul 1 |
Publication series
Name | 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017 |
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Other
Other | 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017 |
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Country/Territory | Korea, Republic of |
City | Jeju |
Period | 17/6/28 → 17/7/1 |
Bibliographical note
Funding Information:ACKNOWLEDGMENT This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (NO.2014-0-00147, Basic Software Research in Human-level Lifelong Machine Learning).
Publisher Copyright:
© 2017 IEEE.
Keywords
- Learning from failure
- Online learning navigation
ASJC Scopus subject areas
- Computer Science Applications
- Biomedical Engineering
- Artificial Intelligence
- Human-Computer Interaction
- Control and Optimization