  Collaborative robots became a popular tool for increasing productivity in
partly automated manufacturing plants. Intuitive robot teaching methods are
required to quickly and flexibly adapt the robot programs to new tasks.
Gestures have an essential role in human communication. However, in
human-robot-interaction scenarios, gesture-based user interfaces are so far
used rarely, and if they employ a one-to-one mapping of gestures to robot
control variables. In this paper, we propose a method that infers the user's
intent based on gesture episodes, the context of the situation, and common
sense. The approach is evaluated in a simulated table-top manipulation setting.
We conduct deterministic experiments with simulated users and show that the
system can even handle personal preferences of each user.
