Human behavior modeling is important for the design and implementation of human-automation interactive control systems. In this context, human behavior refers to a human's control input to systems. We propose a novel method for human behavior modeling that uses human demonstrations for a given task to infer the unknown task objective and the variability. The task objective represents the human's intent or desire. It can be inferred by the inverse optimal control and improve the understanding of human behavior by providing an explainable objective function behind the given human behavior. Meanwhile, the variability denotes the intrinsic uncertainty in human behavior. It can be described by a Gaussian mixture model and capture the uncertainty in human behavior which cannot be encoded by the task objective. The proposed method can improve the prediction accuracy of human behavior by leveraging both task objective and variability. The proposed method is demonstrated through human-subject experiments using an illustrative quadrotor remote control example.

翻译：人类行为建模对于人机交互控制系统的设计与实现至关重要。在此背景下，人类行为指代人类对系统的控制输入。我们提出一种新颖的人类行为建模方法，该方法利用人类在特定任务中的示范数据，推断未知的任务目标与变异性。任务目标反映了人类的意图或期望，可通过逆最优控制进行推断，并通过提供给定人类行为背后的可解释目标函数，增强对人类行为的理解。同时，变异性表征人类行为的内在不确定性，可通过高斯混合模型描述，并捕获任务目标无法编码的人类行为不确定性。所提方法通过同时利用任务目标与变异性，可提升对人类行为的预测精度。通过一个四旋翼飞行器远程控制的示例性人类被试实验，验证了所提方法的有效性。