Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots
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arXiv
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| Format: | Preprint |
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2022
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| author | Mamedov, Shamil Reiter, Rudolf Azad, Seyed Mahdi Basiri Viljoen, Ruan Boedecker, Joschka Diehl, Moritz Swevers, Jan |
| author_facet | Mamedov, Shamil Reiter, Rudolf Azad, Seyed Mahdi Basiri Viljoen, Ruan Boedecker, Joschka Diehl, Moritz Swevers, Jan |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_02941 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots Mamedov, Shamil Reiter, Rudolf Azad, Seyed Mahdi Basiri Viljoen, Ruan Boedecker, Joschka Diehl, Moritz Swevers, Jan Robotics Machine Learning Optimization and Control 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 |
| title | Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots |
| topic | Robotics Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2212.02941 |