A Framework for Realistic Simulation of Daily Human Activity
This work addresses the problem of enabling systematic testing and data generation for social robots in home settings, though it is incremental as it builds on existing simulation methods with added configurability and validation.
The paper tackles the need for realistic simulation of daily human activity in home environments to support social robot development, presenting a framework that generates schedules from templates using a bidirectional constraint propagation algorithm and validates it by producing data resembling human behavior from multiple datasets.
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.