Feature-Based Transfer Learning for Robotic Push Manipulation

Modelling push manipulation so that the outcome of a push can be accurately predicted remains largely an open question in robotics, especially with previously unseen objects. Nevertheless humans possess an internal model of physical interactions that allows them to predict the outcome of an action. We present a transferable learning method for predicting push motion of novel objects.