Application of Gaussian Processes to robot state estimation
| dc.contributor.advisor | Hinne, M. | |
| dc.contributor.advisor | Lanillos Pradas, P. L. | |
| dc.contributor.author | Bakker, Esra | |
| dc.date.issued | 2021-02-08 | |
| dc.description.abstract | Lanillos & Cheng [8] introduced "a computational perceptual model based on predictive processing that enables any multi sensory robot to learn, infer and update its body confi guration when using arbitrary sensors with Gaussian additive noise". Specifi cally, their algorithm works by first using Gaussian Processes (GP) regression to learn the sensor generative model; the mapping between the joint angles of the robot and the sensor values, and then uses this learned generative model to generate a prediction about the state of the robot. A well know problem with GPs is that they don't scale well with a large number of data point or a high number of dimensions. This may make this proposed approach difficult to use for complex robotic systems. In order to asses whether this is a signifi cant concern and whether it can be mitigated I examined how well a variety of their model (using an Extended Kalman Filter) performs on a variety of different robotic systems of increasing complexity. In addition I tested a sparse Gaussian Processes model to investigate its potentia as a method to mitigate the poor scaling of a traditional GP for complex robotic systems. | en_US |
| dc.identifier.uri | https://theses.ubn.ru.nl/handle/123456789/12757 | |
| dc.language.iso | en | en_US |
| dc.thesis.faculty | Faculteit der Sociale Wetenschappen | en_US |
| dc.thesis.specialisation | Bachelor Artificial Intelligence | en_US |
| dc.thesis.studyprogramme | Artificial Intelligence | en_US |
| dc.thesis.type | Bachelor | en_US |
| dc.title | Application of Gaussian Processes to robot state estimation | en_US |
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