  This paper presents an approach to learn online generation of collision-free
and torque-limited trajectories for industrial robots. A neural network, which
is trained via reinforcement learning, is periodically invoked to predict
future motions. For each robot joint, the network outputs the kinematic state
that is desired at the end of the current time interval. Compliance with
kinematic joint limits is ensured by the design of the action space. Given the
current kinematic state and the network prediction, a trajectory for the
current time interval can be computed. The main idea of our paper is to execute
the predicted motion only if a collision-free and torque-limited way to
continue the trajectory is known. In practice, the predicted motion is expanded
by a braking trajectory and simulated using a physics engine. If the simulated
trajectory complies with all safety constraints, the predicted motion is
carried out. Otherwise, the braking trajectory calculated in the previous
decision step serves as an alternative safe behavior. For evaluation, up to
three simulated robots are trained to reach as many randomly placed target
points as possible. We show that our method reliably prevents collisions with
static obstacles and collisions between the robots, while generating motions
that respect both torque limits and kinematic joint limits. Experiments with a
real robot demonstrate that safe trajectories can be generated in real-time.
