Enabling robotic pets to autonomously adapt their own behaviors to enhance therapeutic effects: A data-driven approach

Abstract

Socially-assistive robots (SARs) hold significant potential to transform the management of chronic healthcare conditions (e.g. diabetes, Alzheimer's, dementia) outside the clinic walls. However doing so entails embedding such autonomous robots into people's daily lives and home living environments, which are deeply shaped by the cultural and geographic locations within which they are situated. That begs the question whether we can design autonomous interactive behaviors between SARs and humans based on universal machine learning (ML) and deep learning (DL) models of robotic sensor data that would work across such diverse environments? To investigate this, we conducted a long-term user study with 26 participants across two diverse locations (United States and South Korea) with SARs deployed in each user's home for several weeks. We collected robotic sensor data every second of every day, combined with sophisticated ecological momentary assessment (EMA) sampling techniques, to generate a large-scale dataset of over 270 million data points representing 173 hours of randomly-sampled naturalistic interaction data between the human and SAR. Models built on that data were capable of achieving nearly 84% accuracy for detecting specific interaction modalities (AUC 0.885) when trained/tested on the same location, though suffered significant performance drops when applied to a different location. Further analysis and participant interviews showed that was likely due to differences in home living environments in the US and Korea. The results suggest that our ability to create adaptable behaviors for robotic pets may be dependent on the human-robot interaction (HRI) data available for modeling.