  With the recent advances in machine learning, problems that traditionally
would require accurate modeling to be solved analytically can now be
successfully approached with data-driven strategies. Among these, computing the
inverse kinematics of a redundant robot arm poses a significant challenge due
to the non-linear structure of the robot, the hard joint constraints and the
non-invertible kinematics map. Moreover, most learning algorithms consider a
completely data-driven approach, while often useful information on the
structure of the robot is available and should be positively exploited. In this
work, we present a simple, yet effective, approach for learning the inverse
kinematics. We introduce a structured prediction algorithm that combines a
data-driven strategy with the model provided by a forward kinematics function
-- even when this function is misspecified -- to accurately solve the problem.
The proposed approach ensures that predicted joint configurations are well
within the robot's constraints. We also provide statistical guarantees on the
generalization properties of our estimator as well as an empirical evaluation
of its performance on trajectory reconstruction tasks.
