论文标题
优化有关时间逻辑约束的演示的机器人操纵技巧
Optimizing Demonstrated Robot Manipulation Skills for Temporal Logic Constraints
论文作者
论文摘要
为了执行机器人操纵任务，核心问题是确定满足任务要求的合适轨迹。存在各种计算此类轨迹的方法，正在学习和优化主要驾驶技术。我们的作品建立在学习中的学习（LFD）范式的基础上，专家展示了动作，机器人学会了模仿它们。但是，专家演示不足以捕获各种任务规格，例如掌握对象的时间。在本文中，我们提出了一种新方法，该方法考虑了LFD技能中的正式任务规格。确切地说，我们利用了系统的时间属性的一种表达形式的信号时间逻辑（STL）来制定任务规格并使用黑盒优化（BBO）相应地适应LFD技能。我们使用多个任务展示了我们的方法如何使用STL和BBO解决LFD限制，从而证明了我们在模拟和实际工业环境中的方法。
For performing robotic manipulation tasks, the core problem is determining suitable trajectories that fulfill the task requirements. Various approaches to compute such trajectories exist, being learning and optimization the main driving techniques. Our work builds on the learning-from-demonstration (LfD) paradigm, where an expert demonstrates motions, and the robot learns to imitate them. However, expert demonstrations are not sufficient to capture all sorts of task specifications, such as the timing to grasp an object. In this paper, we propose a new method that considers formal task specifications within LfD skills. Precisely, we leverage Signal Temporal Logic (STL), an expressive form of temporal properties of systems, to formulate task specifications and use black-box optimization (BBO) to adapt an LfD skill accordingly. We demonstrate our approach in simulation and on a real industrial setting using several tasks that showcase how our approach addresses the LfD limitations using STL and BBO.
