  This paper presents a data-efficient approach to learning transferable
forward models for robotic push manipulation. Our approach extends our previous
work on contact-based predictors by leveraging information on the pushed
object's local surface features. We test the hypothesis that, by conditioning
predictions on local surface features, we can achieve generalisation across
objects of different shapes. In doing so, we do not require a CAD model of the
object but rather rely on a point cloud object model (PCOM). Our approach
involves learning motion models that are specific to contact models. Contact
models encode the contacts seen during training time and allow generating
similar contacts at prediction time. Predicting on familiar ground reduces the
motion models' sample complexity while using local contact information for
prediction increases their transferability. In extensive experiments in
simulation, our approach is capable of transfer learning for various test
objects, outperforming a baseline predictor. We support those results with a
proof of concept on a real robot.
