A Framework for Realistic Simulation of Daily Human Activity (2311.15400v1)

Abstract: 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.

• The paper presents a scalable framework that generates realistic daily activity schedules using a bidirectional constraint propagation algorithm.
• The framework supports multi-home simulation and manual configurability to effectively model diverse user personas and activity patterns.
• Evaluation against public and self-collected datasets demonstrates its potential to improve testing and deployment of social robots.

A Framework for Realistic Simulation of Daily Human Activity

Overview

The paper "A Framework for Realistic Simulation of Daily Human Activity" presents a versatile and scalable framework for simulating human activity patterns in home environments. This framework is especially significant for developing and testing social robots like Amazon's Astro, which require interaction and adaptation to daily human movements. The framework is not robot-specific and can be extended to other social robots and smart-home systems aimed at long-term deployment.

The simulation framework includes several key features:

• Manual configurability for different user personas and activity patterns.
• Variation in activity timings.
• Testing across multiple home layouts.

Framework Components

The framework's operation involves multiple steps:

1. Schedule Template Creation: Designers create a schedule template for a simulated user’s day, specifying activities, time constraints, and variability ranges.
2. Schedule Generation: A bidirectional constraint propagation algorithm generates daily activity schedules from these templates.
3. Multi-Home Simulation: Models for various home environments, including navigational floorplans and activity-to-location mappings, are defined to support varied simulation settings.
4. Simulation Execution: The simulation engine executes these schedules, moving simulated users throughout the home based on their activity templates.

Bi-Directional Constraint Propagation Algorithm

The core algorithm facilitates realistic schedule generation by:

• Initialization: Randomly initializing start times and durations based on specified variances.
• Constraint Propagation: Handling duration and adjacency constraints through iterative updates, ensuring consistent and conflict-free schedules.
• Validation: Providing a tool for designers to validate schedule templates, ensuring robustness against under- and over-constraints.

Evaluation and Validation

Use Case Scenarios

The framework was validated using common daily life scenarios:

• Scheduled Activities: Activities with fixed or variable start times.
• Sequence Dependencies: Chains of activities without explicit start times.
• End Time Constraints: Backward-propagated constraints ensuring activities end by a specific time.
• Flexible Activities: Activities filling available time based on other constraints.

Dataset Comparisons

The framework's effectiveness was tested against several public datasets and a self-collected dataset:

• Public Datasets: HOMER, ADL, and GENEActiv datasets, encompassing a variety of daily activities.
• Self-Collected Data: Space transitions within a home environment, tracking real-time positioning.

The evaluation involved fitting manual schedule templates to these datasets and measuring similarity using the Levenshtein distance metric. The generated schedules closely approximated real data, demonstrating the framework's expressive capability.

Implications

Practical Implications

The developed framework has several practical benefits:

• Systematic Testing: Allows for thorough testing of social robot behaviors in diverse home environments.
• Synthetic Data Generation: Supports procedural generation of synthetic datasets for training and validation.
• Bias Minimization: By incorporating diverse scenarios and home layouts, it helps mitigate bias in training data, leading to more robust robot behaviors.

Theoretical Implications

From a theoretical perspective, the ability of the framework to model realistic human activity patterns has implications for:

• Human-Robot Interaction (HRI): Insights gained could inform HRI theories and models, particularly in the domain of adaptive behaviors in dynamic environments.
• Data Representation: The approach demonstrates a balance between manual control and stochastic variability, which could influence future work on data representation and simulation in AI systems.

Future Developments

Future directions may involve:

• Automated Template Fitting: Developing methods to automate the fitting of schedule templates to real datasets.
• Interaction Simulation: Enhancing the framework to model interaction events between users and robots.
• Multi-User Coordination: Addressing the simulation of coordinated activities among multiple users within the same environment.

Conclusion

This work introduces a robust framework for simulating daily human activities, crucial for testing and refining social robots. The framework's manual configurability, combined with its ability to closely emulate real activity patterns, offers a powerful tool for advancing the development and deployment of effective, unbiased social robots for home environments. While primarily focusing on practical applications, the implications of this research extend to improving theoretical models in HRI and AI simulation frameworks.