TY - GEN
T1 - Robust learning from demonstration using leveraged Gaussian processes and sparse-constrained optimization
AU - Choi, Sungjoon
AU - Lee, Kyungjae
AU - Oh, Songhwai
N1 - Funding Information:
This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2013R1A1A2009348) and by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No.B0101-15-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)).
Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/8
Y1 - 2016/6/8
N2 - In this paper, we propose a novel method for robust learning from demonstration using leveraged Gaussian process regression. While existing learning from demonstration (LfD) algorithms assume that demonstrations are given from skillful experts, the proposed method alleviates such assumption by allowing demonstrations from casual or novice users. To learn from demonstrations of mixed quality, we present a sparse-constrained leveraged optimization algorithm using proximal linearized minimization. The proposed sparse constrained leverage optimization algorithm is successfully applied to sensory field reconstruction and direct policy learning for planar navigation problems. In experiments, the proposed sparse-constrained method outperforms existing LfD methods.
AB - In this paper, we propose a novel method for robust learning from demonstration using leveraged Gaussian process regression. While existing learning from demonstration (LfD) algorithms assume that demonstrations are given from skillful experts, the proposed method alleviates such assumption by allowing demonstrations from casual or novice users. To learn from demonstrations of mixed quality, we present a sparse-constrained leveraged optimization algorithm using proximal linearized minimization. The proposed sparse constrained leverage optimization algorithm is successfully applied to sensory field reconstruction and direct policy learning for planar navigation problems. In experiments, the proposed sparse-constrained method outperforms existing LfD methods.
UR - http://www.scopus.com/inward/record.url?scp=84977589548&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2016.7487168
DO - 10.1109/ICRA.2016.7487168
M3 - Conference contribution
AN - SCOPUS:84977589548
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 470
EP - 475
BT - 2016 IEEE International Conference on Robotics and Automation, ICRA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Robotics and Automation, ICRA 2016
Y2 - 16 May 2016 through 21 May 2016
ER -