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This paper presents a data-driven approach to model planar pushinginteraction to predict both the most likely outcome of a push and its expectedvariability. The learned models rely on a variation of Gaussian processes withinput-dependent noise called Variational Heteroscedastic Gaussian processes(VHGP) that capture the mean and variance of a stochastic function. We showthat we can learn accurate models that outperform analytical models after lessthan 100 samples and saturate in performance with less than 1000 samples. Wevalidate the results against a collected dataset of repeated trajectories, anduse the learned models to study questions such as the nature of the variabilityin pushing, and the validity of the quasi-static assumption.

A probabilistic data-driven model for planar pushing