Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control

2025-12-03

AI Summary

This work addresses the challenges of joint tuning of cost weights and low-level controller gains in torque-level nonlinear model predictive control (nMPC) for the UR10e robotic arm, as well as the high risk of poor sim-to-real transfer. We propose a digital twin–based automatic parameter optimization framework. Innovatively, we employ the SAASBO high-dimensional Bayesian optimization algorithm to efficiently search for optimal nMPC parameter configurations within a safety-certified digital twin simulation environment, enabling end-to-end co-tuning across simulation and real hardware. Experimental results demonstrate: (i) a 41.9% reduction in end-effector trajectory tracking error and a 2.5% decrease in online solver time in simulation; and (ii) a 25.8% improvement in tracking accuracy on the physical platform. These results validate the proposed framework’s superior balance of accuracy, real-time performance, and sim-to-real transferability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Automates tuning of torque-based NMPC parameters via Bayesian Optimization
• Enables precise real-time trajectory tracking on a robot arm
• Uses digital twin for safe, efficient high-dimensional parameter exploration

Innovation

• Bayesian optimization auto-tunes torque-level NMPC parameters
• SAASBO with digital twin optimizes high-dimensional control parameters
• Simulation ensures safe transfer to hardware with improved tracking