  Precision robotic manipulation tasks (insertion, screwing, precisely pick,
precisely place) are required in many scenarios. Previous methods achieved good
performance on such manipulation tasks. However, such methods typically require
tedious calibration or expensive sensors. 3D/RGB-D cameras and torque/force
sensors add to the cost of the robotic application and may not always be
economical. In this work, we aim to solve these but using only weak-calibrated
and low-cost webcams. We propose Binocular Alignment Learning (BAL), which
could automatically learn the eye-hand coordination and points alignment
capabilities to solve the four tasks. Our work focuses on working with unknown
eye-hand coordination and proposes different ways of performing eye-in-hand
camera calibration automatically. The algorithm was trained in simulation and
used a practical pipeline to achieve sim2real and test it on the real robot.
Our method achieves a competitively good result with minimal cost on the four
tasks.
