DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Soon-Seo | ko |
dc.contributor.author | Park, Young-Jin | ko |
dc.contributor.author | Choi, Han-Lim | ko |
dc.date.accessioned | 2023-08-08T09:00:49Z | - |
dc.date.available | 2023-08-08T09:00:49Z | - |
dc.date.created | 2023-07-07 | - |
dc.date.issued | 2019-01 | - |
dc.identifier.citation | AIAA Scitech Forum, 2019 | - |
dc.identifier.uri | http://hdl.handle.net/10203/311259 | - |
dc.description.abstract | In this paper, we propose a learning and planning algorithm for a partially observable dynamic system. The Gaussian process state-space model (GPSSM) is adopted to learn the latent dynamics model from the partially observable measurements. GPSSM is a probabilistic dynamical system that represents unknown transition and/or measurement models as the Gaussian Process (GP), and enables the learning of a robust system model from a small number of partially observable time series data. GPSSM is integrated with a variant of Differential Dynamic Programing (DDP) called iterative Linear Quadratic Regulator (iLQR). The proposed method can generate a robust control policy for control/planning. Numerical examples are presented to demonstrate the applicability of the proposed method. | - |
dc.language | English | - |
dc.publisher | American Institute of Aeronautics and Astronautics Inc, AIAA | - |
dc.title | A bayesian approach to learning and planning for partially observable dynamical systems | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85083942340 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | AIAA Scitech Forum, 2019 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | San Diego | - |
dc.identifier.doi | 10.2514/6.2019-0398 | - |
dc.contributor.localauthor | Choi, Han-Lim | - |
dc.contributor.nonIdAuthor | Park, Young-Jin | - |
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