  Robots can now learn how to make decisions and control themselves,
generalizing learned behaviors to unseen scenarios. In particular, AI powered
robots show promise in rough environments like the lunar surface, due to the
environmental uncertainties. We address this critical generalization aspect for
robot locomotion in rough terrain through a training algorithm we have created
called the Path Planning and Motion Control (PPMC) Training Algorithm. This
algorithm is coupled with any generic reinforcement learning algorithm to teach
robots how to respond to user commands and to travel to designated locations on
a single neural network. In this paper, we show that the algorithm works
independent of the robot structure, demonstrating that it works on a wheeled
rover in addition the past results on a quadruped walking robot. Further, we
take several big steps towards real world practicality by introducing a rough
highly uneven terrain. Critically, we show through experiments that the robot
learns to generalize to new rough terrain maps, retaining a 100% success rate.
To the best of our knowledge, this is the first paper to introduce a generic
training algorithm teaching generalized PPMC in rough environments to any
robot, with just the use of reinforcement learning.
