论文标题
机器人钉孔组装的可变合规性控制：一种深厚的加固学习方法
Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach
论文作者
论文摘要
工业机器人操纵器在现代制造业中起着更重要的作用。尽管已进行了广泛研究，但在非结构化环境中安全地解决复杂的高精度组装仍然是一个开放的问题，但钉孔组装是一项常见的工业任务。强化学习（RL）方法已被证明在自动解决操纵任务方面已被证明是成功的。但是，RL仍未在实际机器人系统上广泛采用，因为使用实际硬件需要其他挑战，尤其是在使用位置控制的操纵器时。这项工作的主要贡献是一种基于学习的方法，可以解决孔的位置不确定性的钉孔任务。我们提出了使用多种转移学习技术（SIM2REAL）和域随机化的使用非政策模型的增强学习方法，并引导训练速度。我们针对位置控制机器人的学习框架对各种环境的接触式插入任务进行了广泛的评估。
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.
