Kinematically optimised predictions of object motion

Predicting the motions of rigid objects under contacts is a necessary precursor to planning of robot manipulation of objects. On the one hand physics based rigid body simulations are used, and on the other learning approaches are being developed. The advantage of physics simulations is that because they explicitly perform collision checking they respect kinematic constraints, producing physically plausible predictions. The advantage of learning approaches is that they can capture the effects on motion of unobservable parameters such as mass distribution, and frictional coefficients, thus producing more accurate predicted trajectories. This paper shows how to bring together the advantages of both approaches 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. Combining both approaches results in consistently strong prediction performance. The new simulator outperforms previous learning based approaches on a single contact push manipulation prediction task. We also present results showing that the method works for multi-contact manipulation, for which rigid body simulators are notoriously unstable.