  Complicated assembly processes can be described as a sequence of two main
activities: grasping and insertion. While general grasping solutions are common
in industry, insertion is still only applicable to small subsets of problems,
mainly ones involving simple shapes in fixed locations and in which the
variations are not taken into consideration. Recently, RL approaches with prior
knowledge (e.g., LfD or residual policy) have been adopted. However, these
approaches might be problematic in contact-rich tasks since interaction might
endanger the robot and its equipment. In this paper, we tackled this challenge
by formulating the problem as a regression problem. By combining visual and
force inputs, we demonstrate that our method can scale to 16 different
insertion tasks in less than 10 minutes. The resulting policies are robust to
changes in the socket position, orientation or peg color, as well as to small
differences in peg shape. Finally, we demonstrate an end-to-end solution for 2
complex assembly tasks with multi-insertion objectives when the assembly board
is randomly placed on a table.
