TY - GEN
T1 - Self-correcting online navigation via leveraged Gaussian processes
AU - Chang, Seunggyu
AU - Choi, Sungjoon
AU - Oh, Songhwai
N1 - 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.
PY - 2017/7/25
Y1 - 2017/7/25
N2 - 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.
AB - 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.
KW - Learning from failure
KW - Online learning navigation
UR - http://www.scopus.com/inward/record.url?scp=85034272794&partnerID=8YFLogxK
U2 - 10.1109/URAI.2017.7992850
DO - 10.1109/URAI.2017.7992850
M3 - Conference contribution
AN - SCOPUS:85034272794
T3 - 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017
SP - 868
EP - 873
BT - 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017
Y2 - 28 June 2017 through 1 July 2017
ER -