Contact-rich assembly tasks may result in large and unpredictable forces and torques when the locations of the contacting parts are uncertain. The ability to correct the trajectory in response to haptic feedback and accomplish the task despite location uncertainties is an important skill. We hypothesize that this skill would facilitate generalization and support direct transfer from simulations to real world. To reduce sample complexity, we propose to learn a residual admittance policy (RAP). RAP is learned to correct the movements generated by a baseline policy in the framework of dynamic movement primitives. Given the reference trajectories generated by the baseline policy, the action space of RAP is limited to the admittance parameters. Using deep reinforcement learning, a deep neural network is trained to map task specifications to proper admittance parameters. We demonstrate that RAP handles uncertainties in board location, generalizes well over space, size and shape, and facilitates quick transfer learning. Most impressively, we demonstrate that the policy learned in simulations achieves similar robustness to uncertainties, generalization and performance when deployed on an industrial robot (UR5e) without further training. See accompanying video for demonstrations.