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We consider the problem of a robot learning the mechanical properties ofobjects through physical interaction with the object, and introduce apractical, data-efficient approach for identifying the motion models of theseobjects. The proposed method utilizes a physics engine, where the robot seeksto identify the inertial and friction parameters of the object by simulatingits motion under different values of the parameters and identifying those thatresult in a simulation which matches the observed real motions. The problem issolved in a Bayesian optimization framework. The same framework is used forboth identifying the model of an object online and searching for a policy thatwould minimize a given cost function according to the identified model.Experimental results both in simulation and using a real robot indicate thatthe proposed method outperforms state-of-the-art model-free reinforcementlearning approaches.

Information-theoretic Model Identification and Policy Search using Physics Engines with Application to Robotic Manipulation