  In this paper, we address the problem of vision-based obstacle avoidance for
robotic manipulators. This topic poses challenges for both perception and
motion generation. While most work in the field aims at improving one of those
aspects, we provide a unified framework for approaching this problem. The main
goal of this framework is to connect perception and motion by identifying the
relationship between the visual input and the corresponding motion
representation. To this end, we propose a method for learning reactive obstacle
avoidance policies. We evaluate our method on goal-reaching tasks for single
and multiple obstacles scenarios. We show the ability of the proposed method to
efficiently learn stable obstacle avoidance strategies at a high success rate,
while maintaining closed-loop responsiveness required for critical applications
like human-robot interaction.
