Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots
| Autor: | Mamedov, Shamil, Reiter, Rudolf, Azad, Seyed Mahdi Basiri, Viljoen, Ruan, Boedecker, Joschka, Diehl, Moritz, Swevers, Jan |
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| Rok vydání: | 2022 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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 Comment: Accepted to IROS 2024 |
| Databáze: | arXiv |
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