对家用的私人机器人来说，个性化服务和预先设计的任务同样重要，因为机器人需要根据操作者的习惯调整住宅状况。为了学习由诱因、行为和回报构成的操作者习惯，本文介绍了行为足迹，以描述操作者在家中的行为，并运用逆向增强学习技巧提取用回报函数代表的操作者习惯。本文用一个移动机器人调节室内温度，来实施这个方法，并把该方法和记录操作者所有诱因和行为的基准办法相比较。结果显示，提出的方法可以使机器人准确揭示操作者习惯，并相应地调节环境状况。

For a domestic personal robot, personalized services are as important as predesigned tasks, because the robot needs to adjust the home state based on the operator's habits. An operator's habits are composed of cues, behaviors, and rewards. This article introduces behavioral footprints to describe the operator's behaviors in a house, and applies the inverse reinforcement learning technique to extract the operator's habits, represented by a reward function. We implemented the proposed approach with a mobile robot on indoor temperature adjustment, and compared this approach with a baseline method that recorded all the cues and behaviors of the operator. The result shows that the proposed approach allows the robot to reveal the operator's habits accurately and adjust the environment state accordingly.