CDC 2026 Workshop: Safe and Resilient Learning-Based Control for Robotic Systems

The growing use of robots in safety-critical settings creates a pressing need for control methods that remain reliable under model errors, changing conditions, and uncertain interactions. Learning-based control can improve adaptability and task performance by extracting useful control information from data. However, its deployment is complicated by distribution shifts, incomplete safety assurances, and limited robustness outside nominal operating conditions. This workshop will explore how learning-based methods can be combined with safety- and resilience-oriented control principles to address these limitations. The program will span foundational theory, algorithmic development, and experimental validation, with particular attention to preserving learning flexibility without compromising rigorous safety requirements. Invited speakers will highlight recent advances and demonstrate how these methods have been translated from theoretical frameworks into functioning robotic platforms.