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

Gabriele Fadini; Deepak Ingole; Tong Duy Son; Alisa Rupenyan

Home/Archive/arXiv

arXiv · 2512.03772

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

Gabriele Fadini·Deepak Ingole·Tong Duy Son·Alisa Rupenyan

View original source ↗Read source document ↗Export metadata ↓

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.

Explore related subjects

cs.RO cs.AI cs.SY eess.SY

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Gabriele Fadini, Deepak Ingole, Tong Duy Son, Alisa Rupenyan. 2025-12-03. Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control. https://arxiv.org/abs/2512.03772

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

HybridFlow: A 2-NFE Generative Policy for Real-Time Robotic Manipulation

Generative policies for robotic manipulation must balance action accuracy with inference latency. We present HybridFlow, a three-stage policy inference procedure requiring two network function evaluations (2-NFE). A Global Jump uses the MeanFlow average velocity to generate a coarse action trajectory; a parameter-free ReNoise interpolation constructs a state at a nonzero refinement time; and a Local Refine queries the instantaneous-velocity limit of the same network at that time. This construction reuses a unified model without distillation. Our analysis characterizes interval-composition errors and the attenuation of endpoint error under ReNoise interpolation. Controlled RoboMimic ablations support the MeanFlow proposal and intermediate-state construction, achieving 95% average success with reused noise and 95.5% with fresh noise, versus 78% for one-step MeanFlow. Across five real-robot settings with all policies running on the same Jetson AGX Thor, HybridFlow improves normalized task performance over 16-step Diffusion Policy by 13-68 points with approximately eightfold lower action-generation latency. Additional experiments demonstrate its compatibility as an action expert in a vision-language-action framework. Project page: https://hybridflow-anonymous.pages.dev/

cs.RO↗

arXiv2026

SCRAMPPI: Contingency-Constrained Planning using Hamilton-Jacobi Reachability

Autonomous robots commonly aim to achieve a nominal objective while minimizing a cost. Without contingency preparation, this can leave them vulnerable to mission failure. This is formalized as a trajectory optimization problem over the nominal cost with a contingency constraint: from each checked state along the nominal plan, an admissible contingency maneuver must exist that reaches a designated recovery target within a prescribed contingency horizon. Existing methods either optimize contingency trajectories alongside the nominal plan or evaluate this constraint through computationally expensive nested sampling-based searches. Instead, we introduce SCRAMPPI, which represents this requirement as a reach-avoid problem and leverages Hamilton--Jacobi (HJ) reachability analysis to evaluate contingency feasibility. By either computing a reach-avoid value function offline or updating it only as the environment is revealed, and integrating it with model predictive path integral (MPPI) control via resampling-based rollouts, SCRAMPPI provides contingency-feasibility evaluations while still planning in 15-20 hz. Finally, we present simulated and hardware experiments demonstrating real-time nominal planning and execution of contingency maneuvers in mobile-robot navigation, alongside a five-dimensional friction-limited recovery simulation.

cs.RO↗

arXiv2026

Scenario MPC with STL Specifications and Pareto-Based Feasibility Repair

Temporal logic is a formal language for reasoning about system behaviors over time. Signal temporal logic (STL), in particular, has been used to encode spatio-temporal requirements for control synthesis in multi-agent systems, often under the assumption that agents are cooperative and their dynamics are known. However, real-world multi-agent applications, such as autonomous driving, typically involve stochastic and uncontrollable agents. Recent work explored robust control with worst-case or probabilistic formulations, but remains limited in that it either (1) certifies strict satisfaction of STL constraints without addressing feasibility recovery, or (2) relaxes infeasible constraints with ego-centric objectives. In this paper, we propose a model predictive control (MPC) framework that treats feasibility repair as a Pareto optimization problem to explicitly characterize tradeoffs among agent objectives. We further provide a probabilistic certificate on STL violation rate to formally quantify uncertainty under stochastic and uncontrollable agents. The proposed framework is evaluated on two autonomous driving scenarios. Results show that the framework recovers feasible control with demonstrated safe behaviors.

cs.RO↗