变工况下长期安全学习模型预测控制的经验推荐
Experience Recommendation for Long Term Safe Learning-based Model Predictive Control in Changing Operating Conditions
摘要：
学习将机器人控制性能的前沿推向了新的高度，使机器人能够在以前无法想象的条件下以高性能运行。然而，大多数工作都假设未知部分是静态的或缓慢变化的。这就限制了他们在静态或缓慢变化的环境中工作。然而，在现实世界中，机器人可能会经历各种未知的条件。本文提出了一种方法来扩展现有的单模式GP为基础的安全学习控制器学习越来越多的非线性模型的机器人动力学。我们表明，这种方法使机器人能够重新使用过去的经验，从大量以前访问的操作条件，并安全地适应时，遇到一个新的和不同的操作条件。这使得机器人能够在无需提前指定的大量操作条件下实现安全性和高性能。我们的方法独立于控制器运行，不施加额外的计算时间的控制回路上，无论以前的操作条件考虑的数量。我们证明了我们的方法的有效性，在实验上的900\，kg的地面机器人与物理和人为的变化，其动力学。我们所有的实验都是使用视觉定位进行的。
英文摘要：
Learning has propelled the cutting edge of performance in robotic control to new heights, allowing robots to operate with high performance in conditions that were previously unimaginable. The majority of the work, however, assumes that the unknown parts are static or slowly changing. This limits them to static or slowly changing environments. However, in the real world, a robot may experience various unknown conditions. This paper presents a method to extend an existing single mode GP-based safe learning controller to learn an increasing number of non-linear models for the robot dynamics. We show that this approach enables a robot to re-use past experience from a large number of previously visited operating conditions, and to safely adapt when a new and distinct operating condition is encountered. This allows the robot to achieve safety and high performance in an large number of operating conditions that do not have to be specified ahead of time. Our approach runs independently from the controller, imposing no additional computation time on the control loop regardless of the number of previous operating conditions considered. We demonstrate the effectiveness of our approach in experiment on a 900\,kg ground robot with both physical and artificial changes to its dynamics. All of our experiments are conducted using vision for localization.
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