  Recent breakthroughs in the reinforcement learning (RL) community have made
significant advances towards learning and deploying policies on real world
robotic systems. However, even with the current state-of-the-art algorithms and
computational resources, these algorithms are still plagued with high sample
complexity, and thus long training times, especially for high degree of freedom
(DOF) systems. There are also concerns arising from lack of perceived stability
or robustness guarantees from emerging policies. This paper aims at mitigating
these drawbacks by: (1) modeling a complex, high DOF system with a
representative simple one, (2) making explicit use of forward and inverse
kinematics without forcing the RL algorithm to "learn" them on its own, and (3)
learning locomotion policies in Cartesian space instead of joint space. In this
paper these methods are applied to JPL's Robosimian, but can be readily used on
any system with a base and end effector(s). These locomotion policies can be
produced in just a few minutes, trained on a single laptop. We compare the
robustness of the resulting learned policies to those of other control methods.
An accompanying video for this paper can be found at
https://youtu.be/xDxxSw5ahnc .
