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 adaption
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.

提出了一种基于 AutoML-Zero 的方法 AutoRobotics-Zero（ARZ），该方法能够从零开始发现可以适应环境变化的策略。该方法采用线性寄存器机制来构建具有完整表达能力的控制算法，并在适应突发环境变化时即时调整模型参数和推理算法。在仿真的四足机器人上进行了实验，成功演化出了安全的控制策略，以避免在单个肢体意外折断时造成跌倒。与两种流行的神经网络基准相比，ARZ 方法表现出更大的鲁棒性，并能够构建简单且可解释的控制策略。