Safety-Critical Control
Overview
Safety-critical control studies how to make robots, vehicles, industrial processes, and other dynamical systems obey hard limits while still completing tasks. Common tools include control barrier functions and safety filters, reachability and invariant-set methods, model predictive control, and runtime-assurance shields that intervene on learned or nominal policies. Work also combines these guarantees with reinforcement learning, neural models, and calibrated uncertainty methods such as conformal prediction, with evaluations in navigation, manipulation, autonomous driving, and aerospace. A central challenge is balancing formal guarantees with model error, partial observation, actuator limits, and real-time computation: conservative filters can become infeasible or impede progress, while empirical success alone does not establish safety.
Latest papers
129 papers19 papers in the last four weeks, up 6% on the four weeks before. 0.1% of all new papers.