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.

该研究提出了一种低成本无需昂贵传感器，即可通过学习眼手协调和点对齐能力的方法解决机器人精密操作任务的算法 Binocular Alignment Learning，该算法在仿真和实际机器人测试中都达到了良好的性能。