  We present an automated learning framework for a robotic sketching agent that
is capable of learning stroke-based rendering and motor control simultaneously.
We formulate the robotic sketching problem as a deep decoupled hierarchical
reinforcement learning; two policies for stroke-based rendering and motor
control are learned independently to achieve sub-tasks for drawing, and form a
hierarchy when cooperating for real-world drawing. Without hand-crafted
features, drawing sequences or trajectories, and inverse kinematics, the
proposed method trains the robotic sketching agent from scratch. We performed
experiments with a 6-DoF robot arm with 2F gripper to sketch doodles. Our
experimental results show that the two policies successfully learned the
sub-tasks and collaborated to sketch the target images. Also, the robustness
and flexibility were examined by varying drawing tools and surfaces.
