Learning Inverse Hitting Problem

This paper presents a data collection framework and a learning model to understand the motion of an object after being subject to an impulse. The data collection framework consists of an automated dual arm setup hitting an object to each other, like a collaborative air-hockey game. An impact aware extended Kalman filter is proposed for automation of the air-hockey setup which approximates the discontinuous impulse motion equations through a hitting force model by balancing the energies during collision. To capture the variance in the motion that stochasticity of friction introduces, the errors in the controls for the hitting flux, we model the stochastic relationship between hitting flux and object's resulting displacement, using full density modeling. Further we show the application of the learnt motion model for planning sequential hits with two or more robots, in a Golf-like principle, to enable an object to reach a location far beyond the reach of a single robot.