  Humans often demonstrate diverse behaviors due to their personal preferences,
for instance, related to their individual execution style or personal margin
for safety. In this paper, we consider the problem of integrating both path and
velocity preferences into trajectory planning for robotic manipulators. We
first learn reward functions that represent the user path and velocity
preferences from kinesthetic demonstration. We then optimize the trajectory in
two steps: first the path and then the velocity, to produce trajectories that
adhere to both task requirements and user preferences. We design a set of
parameterized features that capture the fundamental preferences in a
pick-and-place type of object-transportation task, both in shape and timing of
the motion. We demonstrate that our method is capable of generalizing such
preferences to new scenarios. We implement our algorithm on a Franka Emika
7-DoF robot arm, and validate the functionality and flexibility of our approach
in a user study. The results show that non-expert users are able to teach the
robot their preferences with just a few iterations of feedback.
