For the robots to achieve a desired behavior, we can program them directly, train them, or give them an innate driver that makes the robots themselves desire the targeted behavior. With the minimal surprise approach, we implant in our robots the desire to make their world predictable. Here, we apply minimal surprise to collective construction. Simulated robots push blocks in a 2D torus grid world. In two variants of our experiment we either allow for emergent behaviors or predefine the expected environment of the robots. In either way, we evolve robot behaviors that move blocks to structure their environment and make it more predictable. The resulting controllers can be applied in collective construction by robots.

翻译：摘要：为使机器人实现期望行为，我们可以直接编程、训练它们，或赋予其内在驱动力使其自发追求目标行为。通过最小惊喜方法，我们在机器人中植入对世界可预测性的渴望。本文将最小惊喜原理应用于群体构建领域。仿真机器人在二维环形网格世界中推动方块。实验的两种变体中，我们允许涌现行为，或预定义机器人的期望环境。无论采用何种方式，我们均演化出通过移动方块来结构化环境并增强其可预测性的机器人行为。由此产生的控制器可应用于机器人的群体构建。