Robust Learning from Demonstration Using Leveraged Gaussian Processes and Sparse-Constrained Optimization
- Authors
- Choi, Sungjoon; Lee, Kyungjae; Oh, Songhwai
- Issue Date
- May-2016
- Publisher
- IEEE
- Citation
- 2016 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), pp 470 - 475
- Pages
- 6
- Journal Title
- 2016 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)
- Start Page
- 470
- End Page
- 475
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/59379
- DOI
- 10.1109/ICRA.2016.7487168
- ISSN
- 1050-4729
2577-087X
- Abstract
- 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.
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Collections - College of Software > Department of Artificial Intelligence > 1. Journal Articles
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