论文标题
安全模仿非线性模型的灵活机器人预测控制
Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots
论文作者
论文摘要
灵活的机器人可能会克服该行业的一些主要挑战，例如实现本质上安全的人类机器人协作并实现更高的有效载荷比率。但是，由于其复杂的动力学，控制柔性机器人很复杂，包括振荡行为和高维状态空间。非线性模型预测控制（NMPC）提供了一种控制此类机器人的有效手段，但其巨大的计算需求通常会在实时场景中限制其应用。为了快速控制灵活的机器人，我们建议使用模仿学习和预测性安全过滤器的NMPC安全近似。我们的框架大大减少了计算时间，同时又会造成少量的性能损失。与NMPC相比，我们的框架在模拟中控制三维柔性机器人组时，在计算时间上显示了超过八倍的改善，同时保证了安全限制。值得注意的是，我们的方法表现优于最先进的强化学习方法。快速安全近似NMPC的开发具有加速行业中灵活机器人的潜力。该项目代码可在以下网址获得：tinyurl.com/anmpc4fr
Flexible robots may overcome some of the industry's major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher payload-to-mass ratio. However, controlling flexible robots is complicated due to their complex dynamics, which include oscillatory behavior and a high-dimensional state space. Nonlinear model predictive control (NMPC) offers an effective means to control such robots, but its significant computational demand often limits 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 an eightfold improvement in computation time when controlling a three-dimensional flexible robot arm in simulation, all while guaranteeing safety constraints. Notably, our approach outperforms state-of-the-art reinforcement learning methods. The development of fast and safe approximate NMPC holds the potential to accelerate the adoption of flexible robots in industry. The project code is available at: tinyurl.com/anmpc4fr
