  High precision assembly of mechanical parts requires accuracy exceeding the
robot precision. Conventional part mating methods used in the current
manufacturing requires tedious tuning of numerous parameters before deployment.
We show how the robot can successfully perform a tight clearance peg-in-hole
task through training a recurrent neural network with reinforcement learning.
In addition to saving the manual effort, the proposed technique also shows
robustness against position and angle errors for the peg-in-hole task. The
neural network learns to take the optimal action by observing the robot sensors
to estimate the system state. The advantages of our proposed method is
validated experimentally on a 7-axis articulated robot arm.
