  The main novelty of the proposed approach is that it allows a robot to learn
an end-to-end policy which can adapt to changes in the environment during
execution. While goal conditioning of policies has been studied in the RL
literature, such approaches are not easily extended to cases where the robot's
goal can change during execution. This is something that humans are naturally
able to do. However, it is difficult for robots to learn such reflexes (i.e.,
to naturally respond to dynamic environments), especially when the goal
location is not explicitly provided to the robot, and instead needs to be
perceived through a vision sensor. In the current work, we present a method
that can achieve such behavior by combining traditional kinematic planning,
deep learning, and deep reinforcement learning in a synergistic fashion to
generalize to arbitrary environments. We demonstrate the proposed approach for
several reaching and pick-and-place tasks in simulation, as well as on a real
system of a 6-DoF industrial manipulator. A video describing our work could be
found \url{https://youtu.be/hE-Ew59GRPQ}.
