A probabilistic data-driven model for planar pushing

This paper presents a data-driven approach to model planar pushing interaction to predict both the most likely outcome of a push and its expected variability. The learned models rely on a variation of Gaussian processes with input-dependent noise called Variational Heteroscedastic Gaussian processes...

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Bibliographic Details
Published in2017 IEEE International Conference on Robotics and Automation (ICRA) pp. 3008 - 3015
Main Authors Bauza, Maria, Rodriguez, Alberto
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2017
Subjects
Analytical models
Gaussian processes
Kernel
Predictive models
Robots
Training
Trajectory
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Summary:This paper presents a data-driven approach to model planar pushing interaction to predict both the most likely outcome of a push and its expected variability. The learned models rely on a variation of Gaussian processes with input-dependent noise called Variational Heteroscedastic Gaussian processes (VHGP) [1] that capture the mean and variance of a stochastic function. We show that we can learn accurate models that outperform analytical models after less than 100 samples and saturate in performance with less than 1000 samples. We validate the results against a collected dataset of repeated trajectories, and use the learned models to study questions such as the nature of the variability in pushing, and the validity of the quasi-static assumption.
DOI:10.1109/ICRA.2017.7989345