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) 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.

本文提出了一种数据驱动的方法来建模平面推动交互，以预测推动的最可能结果及其期望的可变性，并使用变分异方性高斯过程 (VHGP) 来学习模型。我们展示了该方法只需不到 100 个样本就能学习准确的模型，并在 1000 个样本以内即可达到性能饱和。通过我们收集的轨迹数据集来验证了结果的准确性，并使用学习的模型研究了推动的可变性和准静态假设的有效性。