Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions

Understand

Modern nonlinear control theory seeks to develop feedback controllers that endow systems with properties such as safety and stability.

• The guarantees ensured by these controllers often rely on accurate estimates of the system state for determining control actions.
• In practice, measurement model uncertainty can lead to error in state estimates that degrades these guarantees.
• In this paper, we seek to unify techniques from control theory and machine learning to synthesize controllers that achieve safety in the presence of measurement model uncertainty.