论文标题
使用扭矩控制的机器人，主动动作视觉残留的增强学习，以实现接触量的任务
Proactive Action Visual Residual Reinforcement Learning for Contact-Rich Tasks Using a Torque-Controlled Robot
论文作者
论文摘要
在现代制造环境中通常可以找到接触丰富的操纵任务。但是，对于传统的控制方法，手动设计机器人控制器被认为很难，因为控制器需要有效组合方式和截然不同的特征。在本文中，我们首先考虑将操作空间视觉和触觉信息纳入增强学习（RL）方法，以解决非结构化环境中的目标不确定性问题。此外，我们提出了一个新颖的想法，即引入积极行动来解决部分可观察到的马尔可夫决策过程问题。与这两个想法一起，我们的方法可以适应非结构化环境中合理的变化，并提高政策学习的样本效率。我们在一项任务上评估了我们的方法，该任务涉及使用扭矩控制的机器人插入随机访问存储器，并测试了传统方法中使用的不同基准的成功率。我们证明了我们的方法是强大的，并且可以很好地忍受环境变化。
Contact-rich manipulation tasks are commonly found in modern manufacturing settings. However, manually designing a robot controller is considered hard for traditional control methods as the controller requires an effective combination of modalities and vastly different characteristics. In this paper, we firstly consider incorporating operational space visual and haptic information into reinforcement learning(RL) methods to solve the target uncertainty problem in unstructured environments. Moreover, we propose a novel idea of introducing a proactive action to solve the partially observable Markov decision process problem. Together with these two ideas, our method can either adapt to reasonable variations in unstructured environments and improve the sample efficiency of policy learning. We evaluated our method on a task that involved inserting a random-access memory using a torque-controlled robot, and we tested the success rates of the different baselines used in the traditional methods. We proved that our method is robust and can tolerate environmental variations very well.
