Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914178764111872 |
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| author | Fadini, Gabriele Ingole, Deepak Son, Tong Duy Rupenyan, Alisa |
| author_facet | Fadini, Gabriele Ingole, Deepak Son, Tong Duy Rupenyan, Alisa |
| contents | This paper presents an auto-tuning framework for torque-based Nonlinear Model Predictive Control (nMPC), where the MPC serves as a real-time controller for optimal joint torque commands. The MPC parameters, including cost function weights and low-level controller gains, are optimized using high-dimensional Bayesian Optimization (BO) techniques, specifically Sparse Axis-Aligned Subspace (SAASBO) with a digital twin (DT) to achieve precise end-effector trajectory real-time tracking on an UR10e robot arm. The simulation model allows efficient exploration of the high-dimensional parameter space, and it ensures safe transfer to hardware. Our simulation results demonstrate significant improvements in tracking performance (+41.9%) and reduction in solve times (-2.5%) compared to manually-tuned parameters. Moreover, experimental validation on the real robot follows the trend (with a +25.8% improvement), emphasizing the importance of digital twin-enabled automated parameter optimization for robotic operations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03772 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control Fadini, Gabriele Ingole, Deepak Son, Tong Duy Rupenyan, Alisa Robotics Artificial Intelligence Systems and Control This paper presents an auto-tuning framework for torque-based Nonlinear Model Predictive Control (nMPC), where the MPC serves as a real-time controller for optimal joint torque commands. The MPC parameters, including cost function weights and low-level controller gains, are optimized using high-dimensional Bayesian Optimization (BO) techniques, specifically Sparse Axis-Aligned Subspace (SAASBO) with a digital twin (DT) to achieve precise end-effector trajectory real-time tracking on an UR10e robot arm. The simulation model allows efficient exploration of the high-dimensional parameter space, and it ensures safe transfer to hardware. Our simulation results demonstrate significant improvements in tracking performance (+41.9%) and reduction in solve times (-2.5%) compared to manually-tuned parameters. Moreover, experimental validation on the real robot follows the trend (with a +25.8% improvement), emphasizing the importance of digital twin-enabled automated parameter optimization for robotic operations. |
| title | Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control |
| topic | Robotics Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2512.03772 |