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

Constraint-Guided Online Data Selection for Scalable Data-Driven Safety Filters in Uncertain Robotic Systems