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：在机器人中,在安全限制下优化控制器参数是一项重要挑战。 安全贝耶斯优化(BO)量化了在安全指导在这种环境下进行勘探的目标和限制方面的不确定性。 手工设计一个适当的概率模型可能具有挑战性。 但是,在存在未知的安全限制的情况下,选择可靠的模型超参数对于避免违反安全规定至关重要。 在这里,我们提出通过元学习前程从离线数据中安全BO来解决这一问题的数据驱动方法。 我们以元学习后算法F-PACOH为基础,能够在数据稀缺的情况下提供可靠的不确定性量化。 作为核心贡献,我们制定了一个新的框架,通过经验性不确定性指标和前沿搜索算法,选择符合安全要求的前身。 关于基准功能和高精度运动系统,我们证明我们的元学习前程加快了安全BO方法的趋同,同时保持安全。