  The dominant way to control a robot manipulator uses hand-crafted
differential equations leveraging some form of inverse kinematics / dynamics.
We propose a simple, versatile joint-level controller that dispenses with
differential equations entirely. A deep neural network, trained via model-free
reinforcement learning, is used to map from task space to joint space.
Experiments show the method capable of achieving similar error to traditional
methods, while greatly simplifying the process by automatically handling
redundancy, joint limits, and acceleration / deceleration profiles. The basic
technique is extended to avoid obstacles by augmenting the input to the network
with information about the nearest obstacles. Results are shown both in
simulation and on a real robot via sim-to-real transfer of the learned policy.
We show that it is possible to achieve sub-centimeter accuracy, both in
simulation and the real world, with a moderate amount of training.
