  Nonprehensile manipulation involves long horizon underactuated object
interactions and physical contact with different objects that can inherently
introduce a high degree of uncertainty. In this work, we introduce a novel
Real-to-Sim reward analysis technique, called Riemannian Motion Predictive
Control (RMPC), to reliably imagine and predict the outcome of taking possible
actions for a real robotic platform. Our proposed RMPC benefits from Riemannian
motion policy and second order dynamic model to compute the acceleration
command and control the robot at every location on the surface. Our approach
creates a 3D object-level recomposed model of the real scene where we can
simulate the effect of different trajectories. We produce a closed-loop
controller to reactively push objects in a continuous action space. We evaluate
the performance of our RMPC approach by conducting experiments on a real robot
platform as well as simulation and compare against several baselines. We
observe that RMPC is robust in cluttered as well as occluded environments and
outperforms the baselines.
