Industrial robot manipulators are playing a more significant role in modern manufacturing industries. Though peg-in-hole assembly is a common industrial task which has been extensively researched, safely solving complex high precision assembly in an unstructured environment remains an open problem. Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because working with real hardware entails additional challenges, especially when using position-controlled manipulators. The main contribution of this work is a learning-based method to solve peg-in-hole tasks with position uncertainty of the hole. We proposed the use of an off-policy model-free reinforcement learning method and bootstrap the training speed by using several transfer learning techniques (sim2real) and domain randomization. Our proposed learning framework for position-controlled robots was extensively evaluated on contact-rich insertion tasks on a variety of environments.
翻译:工业机器人操纵者在现代制造业中正在发挥更重要的作用。尽管嵌入孔组装是一项共同的工业任务,已经进行了广泛的研究,但安全地解决在无结构环境中复杂的高精密组装仍然是一个尚未解决的问题。强化学习(RL)方法已被证明成功地自主地解决了操纵任务。然而,在真正的机器人系统中仍然没有被广泛采用RL,因为使用真正的硬件将带来更多的挑战,特别是在使用定位控制的操纵器时。这项工作的主要贡献是一种基于学习的方法,用以在洞的位置不确定的情况下解决嵌入孔任务。我们建议使用多种转让学习技术(模拟技术)和域随机化方法,采用非政策型强化学习方法和靴子陷阱训练速度。我们对定位控制机器人的拟议学习框架进行了广泛的评价,以在各种环境中进行接触丰富的插入任务。