Flexible robots may overcome some of the industry's major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher load-to-mass ratio. However, controlling flexible robots is complicated due to their complex dynamics, which include oscillatory behavior and a high-dimensional state space. NMPC offers an effective means to control such robots, but its extensive computational demands often limit its application in real-time scenarios. To enable fast control of flexible robots, we propose a framework for a safe approximation of NMPC using imitation learning and a predictive safety filter. Our framework significantly reduces computation time while incurring a slight loss in performance. Compared to NMPC, our framework shows more than a eightfold improvement in computation time when controlling a three-dimensional flexible robot arm in simulation, all while guaranteeing safety constraints. Notably, our approach outperforms conventional reinforcement learning methods. The development of fast and safe approximate NMPC holds the potential to accelerate the adoption of flexible robots in industry.

翻译：柔性机器人有望克服工业领域的一些重大挑战，例如实现本质安全的人机协作以及获得更高的负载质量比。然而，由于其复杂的动力学特性（包括振荡行为和高维状态空间），柔性机器人的控制较为复杂。非线性模型预测控制（NMPC）为控制此类机器人提供了有效手段，但其巨大的计算需求往往限制了其在实时场景中的应用。为实现对柔性机器人的快速控制，我们提出了一种基于模仿学习和预测安全滤波器的NMPC安全近似框架。该框架在轻微性能损失下显著降低了计算时间。在仿真中控制三维柔性机器人手臂时，与NMPC相比，我们的框架计算时间提升了八倍以上，同时确保了安全约束。值得注意的是，我们的方法优于传统强化学习方法。快速且安全的近似NMPC的发展有望加速柔性机器人在工业中的应用。