Certainty Equivalent Perception-Based Control

Machine learning provides a promising avenue for incorporating rich sensing modalities into autonomous systems. However, our coarse understanding of how ML systems fail limits the adoption of data-driven techniques in real-world applications. In particular, applications involving feedback require that errors do not accumulate and lead to instability. In this work, we propose and analyze a baseline method for incorporating a learning-enabled component into closed-loop control, providing bounds on the sample complexity of a reference tracking problem. Much recent work on developing guarantees for learning and control has focused on the case that dynamics are unknown [Dean et al., 2017, Simchowitz and Foster, 2020, Mania et al., 2020].