  Space exploration missions have seen use of increasingly sophisticated
robotic systems with ever more autonomy. Deep learning promises to take this
even a step further, and has applications for high-level tasks, like path
planning, as well as low-level tasks, like motion control, which are critical
components for mission efficiency and success. Using deep reinforcement
end-to-end learning with randomized reward function parameters during training,
we teach a simulated 8 degree-of-freedom quadruped ant-like robot to travel
anywhere within a perimeter, conducting path plan and motion control on a
single neural network, without any system model or prior knowledge of the
terrain or environment. Our approach also allows for user specified waypoints,
which could translate well to either fully autonomous or
semi-autonomous/teleoperated space applications that encounter delay times. We
trained the agent using randomly generated waypoints linked to the reward
function and passed waypoint coordinates as inputs to the neural network. Such
applications show promise on a variety of space exploration robots, including
high speed rovers for fast locomotion and legged cave robots for rough terrain.
