Self-Modeling is the process by which an agent, such as an animal or machine, learns to create a predictive model of its own dynamics. Once captured, this self-model can then allow the agent to plan and evaluate various potential behaviors internally using the self-model, rather than using costly physical experimentation. Here, we quantify the benefits of such self-modeling against the complexity of the robot. We find a R2 =0.90 correlation between the number of degrees of freedom a robot has, and the added value of self-modeling as compared to a direct learning baseline. This result may help motivate self modeling in increasingly complex robotic systems, as well as shed light on the origins of self-modeling, and ultimately self-awareness, in animals and humans.

翻译：自建模型是一个代理物,如动物或机器,学会创建其动态的预测模型的过程。 一旦捕获, 这种自建模型可以允许代理商使用自建模型来规划和评估内部的各种潜在行为, 而不是使用昂贵的物理实验。 在这里, 我们量化这种自建模型对机器人的复杂性的好处。 我们发现机器人拥有的自由度数量与自我建模相对于直接学习基线的附加值之间有R2=0.90的关联。 这一结果可能有助于激励机器人系统日益复杂的自我建模, 以及揭示自建的起源, 并最终揭示动物和人类的自我意识。