GoSafe: Globally Optimal Safe Robot Learning
This work addresses safety constraints in robotic learning, which is crucial for preventing hardware damage, though it is incremental as it builds on existing SafeOpt methods.
The paper tackles the problem of learning safe robot policies by extending SafeOpt to explore beyond initial safe regions while maintaining safety guarantees, and demonstrates convergence to the global optimum with hardware validation.
When learning policies for robotic systems from data, safety is a major concern, as violation of safety constraints may cause hardware damage. SafeOpt is an efficient Bayesian optimization (BO) algorithm that can learn policies while guaranteeing safety with high probability. However, its search space is limited to an initially given safe region. We extend this method by exploring outside the initial safe area while still guaranteeing safety with high probability. This is achieved by learning a set of initial conditions from which we can recover safely using a learned backup controller in case of a potential failure. We derive conditions for guaranteed convergence to the global optimum and validate GoSafe in hardware experiments.