  Planning robust robot manipulation requires good forward models that enable
robust plans to be found. This work shows how to achieve this using a forward
model learned from robot data to plan push manipulations. We explore learning
methods (Gaussian Process Regression, and an Ensemble of Mixture Density
Networks) that give estimates of the uncertainty in their predictions. These
learned models are utilised by a model predictive path integral (MPPI)
controller to plan how to push the box to a goal location. The planner avoids
regions of high predictive uncertainty in the forward model. This includes both
inherent uncertainty in dynamics, and meta uncertainty due to limited data.
Thus, pushing tasks are completed in a robust fashion with respect to estimated
uncertainty in the forward model and without the need of differentiable cost
functions. We demonstrate the method on a real robot, and show that learning
can outperform physics simulation. Using simulation, we also show the ability
to plan uncertainty averse paths.
