  This paper is concerned with learning transferable forward models for push
manipulation that can be applying to novel contexts and how to improve the
quality of prediction when critical information is available. We propose to
learn a parametric internal model for push interactions that, similar for
humans, enables a robot to predict the outcome of a physical interaction even
in novel contexts. Given a desired push action, humans are capable to identify
where to place their finger on a new object so to produce a predictable motion
of the object. We achieve the same behaviour by factorising the learning into
two parts. First, we learn a set of local contact models to represent the
geometrical relations between the robot pusher, the object, and the
environment. Then we learn a set of parametric local motion models to predict
how these contacts change throughout a push. The set of contact and motion
models represent our internal model. By adjusting the shapes of the
distributions over the physical parameters, we modify the internal model's
response. Uniform distributions yield to coarse estimates when no information
is available about the novel context (i.e. unbiased predictor). A more accurate
predictor can be learned for a specific environment/object pair (e.g. low
friction/high mass), i.e. biased predictor. The effectiveness of our approach
is shown in a simulated environment in which a Pioneer 3-DX robot needs to
predict a push outcome for a novel object, and we provide a proof of concept on
a real robot. We train on 2 objects (a cube and a cylinder) for a total of
24,000 pushes in various conditions, and test on 6 objects encompassing a
variety of shapes, sizes, and physical parameters for a total of 14,400
predicted push outcomes. Our results show that both biased and unbiased
predictors can reliably produce predictions in line with the outcomes of a
carefully tuned physics simulator.
