  Several publications are concerned with learning inverse kinematics, however,
their evaluation is often limited and none of the proposed methods is of
practical relevance for rigid-body kinematics with a known forward model. We
argue that for rigid-body kinematics one of the first proposed machine learning
(ML) solutions to inverse kinematics -- distal teaching (DT) -- is actually
good enough when combined with differentiable programming libraries and we
provide an extensive evaluation and comparison to analytical and numerical
solutions. In particular, we analyze solve rate, accuracy, sample efficiency
and scalability. Further, we study how DT handles joint limits, singularities,
unreachable poses, trajectories and provide a comparison of execution times.
The three approaches are evaluated on three different rigid body mechanisms
with varying complexity. With enough training data and relaxed precision
requirements, DT has a better solve rate and is faster than state-of-the-art
numerical solvers for a 15-DoF mechanism. DT is not affected by singularities
while numerical solutions are vulnerable to them. In all other cases numerical
solutions are usually better. Analytical solutions outperform the other
approaches by far if they are available.
