论文标题
通过双眼视觉增强学习，经济的精确操纵和自动眼手协调
Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learning
论文作者
论文摘要
在许多情况下，需要精确的机器人操纵任务（插入，拧紧，精确选择，精确选择）。以前的方法在此类操作任务上实现了良好的性能。但是，这种方法通常需要乏味的校准或昂贵的传感器。 3D/RGB-D摄像机和扭矩/力传感器增加了机器人应用的成本，并且可能并不总是经济的。在这项工作中，我们旨在解决这些问题，但仅使用弱化和低成本的网络摄像头。我们提出了双眼对准学习（BAL），可以自动学习眼手协调，并点对准能力来解决这四个任务。我们的工作重点是与未知的眼睛协调合作，并提出了自动执行眼镜校准的不同方法。该算法在模拟中进行了训练，并使用实用管道来实现SIM2Real并在真实机器人上进行测试。我们的方法在四个任务上成本最低，取得了竞争性的效果。
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.
