  There is an increased demand for task automation in robots. Contact-rich
tasks, wherein multiple contact transitions occur in a series of operations,
are extensively being studied to realize high accuracy. In this study, we
propose a methodology that uses reinforcement learning (RL) to achieve high
performance in robots for the execution of assembly tasks that require precise
contact with objects without causing damage. The proposed method ensures the
online generation of stiffness matrices that help improve the performance of
local trajectory optimization. The method has an advantage of rapid response
owing to short sampling time of the trajectory planning. The effectiveness of
the method was verified via experiments involving two contact-rich tasks. The
results indicate that the proposed method can be implemented in various
contact-rich manipulations. A demonstration video shows the performance.
(https://youtu.be/gxSCl7Tp4-0)
