In robotics, simulation has the potential to reduce design time and costs, and lead to a more robust engineered solution and a safer development process. However, the use of simulators is predicated on the availability of good models. This contribution is concerned with improving the quality of these models via calibration, which is cast herein in a Bayesian framework. First, we discuss the Bayesian machinery involved in model calibration. Then, we demonstrate it in one example: calibration of a vehicle dynamics model that has low degree of freedom count and can be used for state estimation, model predictive control, or path planning. A high fidelity simulator is used to emulate the ``experiments'' and generate the data for the calibration. The merit of this work is not tied to a new Bayesian methodology for calibration, but to the demonstration of how the Bayesian machinery can establish connections among models in computational dynamics, even when the data in use is noisy. The software used to generate the results reported herein is available in a public repository for unfettered use and distribution.

翻译：在机器人学中，仿真具有缩短设计时间和降低成本的潜力，能够带来更鲁棒的工程解决方案以及更安全的设计流程。然而，仿真器使用的前提是具备良好的模型。本文致力于通过校准提升模型质量，并在贝叶斯框架下对此问题进行阐述。首先，我们讨论了模型校准所涉及的贝叶斯机制。随后，通过一个示例进行演示：对具有低自由度数、可用于状态估计、模型预测控制或路径规划的车辆动力学模型进行校准。高保真度仿真器被用于模拟"实验"并生成校准数据。本工作的价值不在于提出新的贝叶斯校准方法，而在于演示贝叶斯机制如何能够在计算动力学中建立模型间的关联，即使所使用的数据含有噪声。生成本文报告结果所使用的软件已存放于公共代码仓库中，供自由使用与分发。