# Learning Based NMPC Adaptation for Autonomous Driving using Parallelized Digital Twin

arXiv: https://arxiv.org/abs/2402.16645 · HTML: https://mlsanity.com/paper/2402.16645/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving.
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Placed in

- End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/)

## Builds on (most cited first, 16 of 16)

- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [How Simulation Helps Autonomous Driving:A Survey of Sim2real, Digital Twins, and Parallel Intelligence](https://mlsanity.com/paper/2305.01263/) (2023)
- [Assessing Transferability from Simulation to Reality for Reinforcement Learning](https://mlsanity.com/paper/1907.04685/) (2019)
- [Adaptive Adjustment of Noise Covariance in Kalman Filter for Dynamic State Estimation](https://mlsanity.com/paper/1702.00884/) (2017)
- [Online Dynamics Learning for Predictive Control with an Application to Aerial Robots](https://mlsanity.com/paper/2207.09344/) (2022)
- [Adaptive MPC for Iterative Tasks](https://mlsanity.com/paper/1804.09831/) (2018)
- [Adaptive Model Predictive Control for High-Accuracy Trajectory Tracking in Changing Conditions](https://mlsanity.com/paper/1807.05290/) (2018)
- [Contextual Tuning of Model Predictive Control for Autonomous Racing](https://mlsanity.com/paper/2110.02710/) (2021)
- [Sim2real for Autonomous Vehicle Control using Executable Digital Twin](https://mlsanity.com/paper/2208.06226/) (2022)
- [A General Framework for Learning-Based Distributionally Robust MPC of Markov Jump Systems](https://mlsanity.com/paper/2106.00561/) (2021)
- [Evaluation of MPC-based Imitation Learning for Human-like Autonomous Driving](https://mlsanity.com/paper/2211.12111/) (2022)
- [Bayesian Optimization for Policy Search in High-Dimensional Systems via Automatic Domain Selection](https://mlsanity.com/paper/2001.07394/) (2020)
- [Automated Controller Calibration by Kalman Filtering](https://mlsanity.com/paper/2111.10832/) (2021)
- [The Executable Digital Twin: merging the digital and the physics worlds](https://mlsanity.com/paper/2210.17402/) (2022)
- [The Twin-in-the-Loop approach for vehicle dynamics control](https://mlsanity.com/paper/2209.02263/) (2022)
- [Safety Envelope for Orthogonal Collocation Methods in Embedded Optimal Control](https://mlsanity.com/paper/2211.14853/) (2022)

## Built on by (most cited first, 2 of 2)

- [Iterative Tuning of Nonlinear Model Predictive Control for Robotic Manufacturing Tasks](https://mlsanity.com/paper/2512.13170/) (2025)
- [Information-Theoretic Framework for Self-Adapting Model Predictive Controllers](https://mlsanity.com/paper/2603.01286/) (2026)

## Abstract (arXiv)

In this work, we focus on the challenge of transferring an autonomous driving controller from simulation to the real world (i.e. Sim2Real). We propose a data-efficient method for online and on-the-fly adaptation of parametrizable control architectures such that the target closed-loop performance is optimized while accounting for uncertainties as model mismatches, changes in the environment, and task variations. The novelty of the approach resides in leveraging black-box optimization enabled by Executable Digital Twins (xDTs) for data-driven parameter calibration through derivative-free methods to directly adapt the controller in real-time. The xDTs are augmented with Domain Randomization for robustness and allow for safe parameter exploration. The proposed method requires a minimal amount of interaction with the real-world as it pushes the exploration towards the xDTs. We validate our approach through real-world experiments, demonstrating its effectiveness in transferring and fine-tuning a NMPC with 9 parameters, in under 10 minutes. This eliminates the need for hours-long manual tuning and lengthy machine learning training and data collection phases. Our results show that the online adapted NMPC directly compensates for the Sim2Real gap and avoids overtuning in simulation. Importantly, a 75% improvement in tracking performance is achieved and the Sim2Real gap over the target performance is reduced from a factor of 876 to 1.033.