  Collaborative robots offer increased interaction capabilities at relatively
low cost but in contrast to their industrial counterparts they inevitably lack
precision. Moreover, in addition to the robots' own imperfect models,
day-to-day operations entail various sources of errors that despite being small
rapidly accumulate. This happens as tasks change and robots are re-programmed,
often requiring time-consuming calibrations. These aspects strongly limit the
application of collaborative robots in tasks demanding high precision (e.g.
watch-making). We address this problem by relying on a dual-arm system with
laser-based sensing to measure relative poses between objects of interest and
compensate for pose errors coming from robot proprioception. Our approach
leverages previous knowledge of object 3D models in combination with point
cloud registration to efficiently extract relevant poses and compute corrective
trajectories. This results in high-precision assembly behaviors. The approach
is validated in a needle threading experiment, with a 150{\mu}m thread and a
300{\mu}m hole, and a USB insertion task using two 7-axis Panda robots.
