  In this paper, we present a learning-based approach that allows a robot to
quickly follow a reference path defined in joint space without exceeding limits
on the position, velocity, acceleration and jerk of each robot joint. Contrary
to offline methods for time-optimal path parameterization, the reference path
can be changed during motion execution. In addition, our approach can utilize
sensory feedback, for instance, to follow a reference path with a bipedal robot
without losing balance. With our method, the robot is controlled by a neural
network that is trained via reinforcement learning using data generated by a
physics simulator. From a mathematical perspective, the problem of tracking a
reference path in a time-optimized manner is formalized as a Markov decision
process. Each state includes a fixed number of waypoints specifying the next
part of the reference path. The action space is designed in such a way that all
resulting motions comply with the specified kinematic joint limits. The reward
function finally reflects the trade-off between the execution time, the
deviation from the desired reference path and optional additional objectives
like balancing. We evaluate our approach with and without additional objectives
and show that time-optimized path tracking can be successfully learned for both
industrial and humanoid robots. In addition, we demonstrate that networks
trained in simulation can be successfully transferred to a real robot.
