# A probabilistic data-driven model for planar pushing

## TL;DR

This paper introduces a data-driven probabilistic model for planar pushing interactions using Variational Heteroscedastic Gaussian processes, achieving high accuracy with minimal data and enabling analysis of pushing variability.

## Contribution

The paper develops a novel VHGP-based model for planar pushing that outperforms analytical models with fewer samples and captures outcome variability effectively.

## Key findings

- Accurate predictions with less than 100 samples
- Model performance saturates with fewer than 1000 samples
- Validated against collected trajectory data

## Abstract

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.