In robotics, optimizing controller parameters under safety constraints is an important challenge. Safe Bayesian optimization (BO) quantifies uncertainty in the objective and constraints to safely guide exploration in such settings. Hand-designing a suitable probabilistic model can be challenging, however. In the presence of unknown safety constraints, it is crucial to choose reliable model hyper-parameters to avoid safety violations. Here, we propose a data-driven approach to this problem by meta-learning priors for safe BO from offline data. We build on a meta-learning algorithm, F-PACOH, capable of providing reliable uncertainty quantification in settings of data scarcity. As core contribution, we develop a novel framework for choosing safety-compliant priors in a data-riven manner via empirical uncertainty metrics and a frontier search algorithm. On benchmark functions and a high-precision motion system, we demonstrate that our meta-learned priors accelerate the convergence of safe BO approaches while maintaining safety.

翻译：在机器人学中，在安全约束下优化控制器参数是一项重要挑战。安全贝叶斯优化通过对目标函数和约束条件进行不确定性量化，在此类场景中安全地引导探索过程。然而，人工设计合适的概率模型颇具难度。在存在未知安全约束的情况下，选择可靠的模型超参数以避免违反安全规范至关重要。本文提出了一种数据驱动方法，通过从离线数据中元学习安全贝叶斯优化的先验来解决该问题。我们基于元学习算法F-PACOH构建框架，该算法能够在数据稀缺场景中提供可靠的不确定性量化。作为核心贡献，我们开发了一种新型框架，通过经验不确定性指标和边界搜索算法，以数据驱动的方式选择符合安全要求的先验。在基准函数和高精度运动系统上的实验表明，我们元学习得到的先验在保持安全性的同时，加速了安全贝叶斯优化方法的收敛过程。