As the use of autonomous robotic systems expands in tasks that are complex and challenging to model, the demand for robust data-driven control methods that can certify safety and stability in uncertain conditions is increasing. However, the practical implementation of these methods often faces scalability issues due to the growing amount of data points with system complexity, and a significant reliance on high-quality training data. In response to these challenges, this study presents a scalable data-driven controller that efficiently identifies and infers from the most informative data points for implementing data-driven safety filters. Our approach is grounded in the integration of a model-based certificate function-based method and Gaussian Process (GP) regression, reinforced by a novel online data selection algorithm that reduces time complexity from quadratic to linear relative to dataset size. Empirical evidence, gathered from successful real-world cart-pole swing-up experiments and simulated locomotion of a five-link bipedal robot, demonstrates the efficacy of our approach. Our findings reveal that our efficient online data selection algorithm, which strategically selects key data points, enhances the practicality and efficiency of data-driven certifying filters in complex robotic systems, significantly mitigating scalability concerns inherent in nonparametric learning-based control methods.

翻译：随着自主机器人系统在复杂且难以建模的任务中的应用日益扩展，对能够在不确定条件下保障安全性和稳定性的鲁棒数据驱动控制方法的需求正在增加。然而，这些方法的实际实施常面临可扩展性问题，这源于随系统复杂度增加的数据点数量增长，以及对高质量训练数据的显著依赖。针对这些挑战，本研究提出一种可扩展的数据驱动控制器，能够高效识别并利用最具信息量的数据点来实现数据驱动安全滤波器。我们的方法基于模型化证书函数方法与高斯过程回归的融合，并通过一种新颖的在线数据选择算法加以强化，该算法将时间复杂度从数据集的二次方降至线性。通过成功的真实世界推车摆杆摆动实验以及五连杆双足机器人的模拟运动所收集的实证证据，验证了我们方法的有效性。研究结果表明，我们高效的在线数据选择算法通过战略性选择关键数据点，提升了复杂机器人系统中数据驱动认证滤波器的实用性和效率，显著缓解了非参数化学习控制方法固有的可扩展性问题。