Robot Model Learning with Gaussian Process Mixture Model

Park, Sooho; Huang, Yu; Goh, Chun Fan; Shimada, Kenji

doi:10.1109/coase.2018.8560452

Cited by 1 publication

(1 citation statement)

References 10 publications

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“…GPR models and kriging methods are applicable to a wide variety of problems such as modeling or control of robotics-related applications, the prediction and estimation of temperature, precipitation, missing pixels and unmixing of pixels in hyperspectral imaging (HSI), human head pose estimation, concentration of carbon dioxide in the atmosphere, etc. [22,[26][27][28][29][30][31][32][33][34][35]. As an example in HSI, one main objective is to unmix the spectral information to make an inference of the composing materials in the scene.…”

Section: Introductionmentioning

confidence: 99%

Exploration vs. Data Refinement via Multiple Mobile Sensors

Shekaramiz

Moon

Gunther

2019

Entropy

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We examine the deployment of multiple mobile sensors to explore an unknown region to map regions containing concentration of a physical quantity such as heat, electron density, and so on. The exploration trades off between two desiderata: to continue taking data in a region known to contain the quantity of interest with the intent of refining the measurements vs. taking data in unobserved areas to attempt to discover new regions where the quantity may exist. Making reasonable and practical decisions to simultaneously fulfill both goals of exploration and data refinement seem to be hard and contradictory. For this purpose, we propose a general framework that makes value-laden decisions for the trajectory of mobile sensors. The framework employs a Gaussian process regression model to predict the distribution of the physical quantity of interest at unseen locations. Then, the decision-making on the trajectories of sensors is performed using an epistemic utility controller. An example is provided to illustrate the merit and applicability of the proposed framework.

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