For social robots like Astro which interact with and adapt to the daily movements of users within the home, realistic simulation of human activity is needed for feature development and testing. This paper presents a framework for simulating daily human activity patterns in home environments at scale, supporting manual configurability of different personas or activity patterns, variation of activity timings, and testing on multiple home layouts. We introduce a method for specifying day-to-day variation in schedules and present a bidirectional constraint propagation algorithm for generating schedules from templates. We validate the expressive power of our framework through a use case scenario analysis and demonstrate that our method can be used to generate data closely resembling human behavior from three public datasets and a self-collected dataset. Our contribution supports systematic testing of social robot behaviors at scale, enables procedural generation of synthetic datasets of human movement in different households, and can help minimize bias in training data, leading to more robust and effective robots for home environments.

翻译：对于像Astro这类与家庭环境中用户日常活动进行交互并自适应调整的社交机器人而言，人类活动的真实仿真对功能开发与测试至关重要。本文提出了一套大规模模拟家庭环境中日常人类活动模式的框架，支持对不同角色或活动模式的手动配置、活动时间的灵活调整，以及多户型布局的测试。我们引入了一种描述日程表逐日变化的方法，并提出了一个双向约束传播算法用于从模板生成日程表。通过用例场景分析验证了框架的表达能力，实验表明该方法能生成与三个公开数据集及自采集数据集中人类行为高度相似的数据。本项贡献可支持社交机器人行为的大规模系统性测试，实现不同家庭场景下人类活动轨迹合成数据集的程序化生成，并有助于最小化训练数据偏差，最终构建更鲁棒、更高效的家居环境机器人系统。