  Mobile manipulator throwing is a promising method to increase the flexibility
and efficiency of dynamic manipulation in factories. Its major challenge is to
efficiently plan a feasible throw under a wide set of task specifications. We
show that the mobile manipulator throwing problem can be simplified to a planar
problem, hence greatly reducing the computational costs. Using machine learning
approaches, we build a model of the object's inverted flying dynamics and the
robot's kinematic feasibility, which enables throwing motion generation within
1 ms for given query of target position. Thanks to the computational efficiency
of our method, we show that the system is adaptive under disturbance, via
replanning on the fly for alternative solutions, instead of sticking to the
original throwing plan.
