  Autonomous robots deployed in the real world will need control policies that
rapidly adapt to environmental changes. To this end, we propose
AutoRobotics-Zero (ARZ), a method based on AutoML-Zero that discovers zero-shot
adaptable policies from scratch. In contrast to neural network adaptation
policies, where only model parameters are optimized, ARZ can build control
algorithms with the full expressive power of a linear register machine. We
evolve modular policies that tune their model parameters and alter their
inference algorithm on-the-fly to adapt to sudden environmental changes. We
demonstrate our method on a realistic simulated quadruped robot, for which we
evolve safe control policies that avoid falling when individual limbs suddenly
break. This is a challenging task in which two popular neural network baselines
fail. Finally, we conduct a detailed analysis of our method on a novel and
challenging non-stationary control task dubbed Cataclysmic Cartpole. Results
confirm our findings that ARZ is significantly more robust to sudden
environmental changes and can build simple, interpretable control policies.
