Predicting the motions of rigid objects under contacts is a necessary precursor to planning of robot manipulation of objects. We show how to bring together the advantages of learning approaches and kinematic optimization to achieve learned simulators of specific objects that outperform previous learning approaches. Our approach employs a fast simplified collision checker and a learning method. The learner predicts trajectories for the object. These are optimised post prediction to minimise interpenetrations according to the collision checker. In addition we show that cleaning the training data prior to learning can also improve performance.
D. Belter, M. Kopicki, S. Zurek, J. Wyatt, Kinematically Optimised Predictions of Object Motion, IEEE/RSJ 2014 International Conference on Intelligent Robots and Systems, Chicago, USA, pp. 4422-4427, 2014