  Detecting and adapting to catastrophic failures in robotic systems requires a
robot to learn its new dynamics quickly and safely to best accomplish its
goals. To address this challenging problem, we propose probabilistically-safe,
online learning techniques to infer the altered dynamics of a robot at the
moment a failure (e.g., physical damage) occurs. We combine model predictive
control and active learning within a chance-constrained optimization framework
to safely and efficiently learn the new plant model of the robot. We leverage a
neural network for function approximation in learning the latent dynamics of
the robot under failure conditions. Our framework generalizes to various damage
conditions while being computationally light-weight to advance real-time
deployment. We empirically validate within a virtual environment that we can
regain control of a severely damaged aircraft in seconds and require only 0.1
seconds to find safe, information-rich trajectories, outperforming
state-of-the-art approaches.
