  In this work, we contribute a large-scale study benchmarking the performance
of multiple motion-based learning from demonstration approaches. Given the
number and diversity of existing methods, it is critical that comprehensive
empirical studies be performed comparing the relative strengths of these
learning techniques. In particular, we evaluate four different approaches based
on properties an end user may desire for real-world tasks. To perform this
evaluation, we collected data from nine participants, across four different
manipulation tasks with varying starting conditions. The resulting
demonstrations were used to train 180 task models and evaluated on 720 task
reproductions on a physical robot. Our results detail how i) complexity of the
task, ii) the expertise of the human demonstrator, and iii) the starting
configuration of the robot affect task performance. The collected dataset of
demonstrations, robot executions, and evaluations are being made publicly
available. Research insights and guidelines are also provided to guide future
research and deployment choices about these approaches.
