As the use of autonomous robots 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：随着自主机器人在复杂且难以建模任务中的应用日益广泛，对能够在不确定条件下保证安全性与稳定性的鲁棒数据驱动控制方法的需求不断增长。然而，这些方法在实际应用中常面临可扩展性问题，原因在于系统复杂度增加导致数据点数量激增，以及对高质量训练数据的严重依赖。为应对这些挑战，本研究提出一种可扩展的数据驱动控制器，该控制器能高效识别并基于最具信息量的数据点进行推理，以实施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（GP）回归的融合，并通过一种新颖的在线数据选择算法得到强化，该算法将时间复杂度相对于数据集大小从二次降低至线性。从真实世界倒立摆起摆实验及五连杆双足机器人运动仿真中获得的实证证据，验证了本方法的有效性。研究结果表明，我们通过策略性选择关键数据点的高效在线数据选择算法，增强了复杂机器人系统中数据驱动认证滤波器的实用性与效率，显著缓解了基于非参数学习的控制方法固有的可扩展性问题。