Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots

• URL: http://arxiv.org/abs/2212.02941v3
• Date: Wed, 14 Aug 2024 20:40:17 GMT
• Title: Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots
• Authors: Shamil Mamedov, Rudolf Reiter, Seyed Mahdi Basiri Azad, Ruan Viljoen, Joschka Boedecker, Moritz Diehl, Jan Swevers,
• Abstract summary: We propose a framework for a safe approximation of model predictive control (NMPC) using imitation learning and a predictive safety filter. Compared to NMPC, our framework shows more than an eightfold improvement in computation time when controlling a three-dimensional flexible robot arm in simulation. The development of fast and safe approximate NMPC holds the potential to accelerate the adoption of flexible robots in industry.
• Score: 6.501150406218775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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