A Robot Just for You: Multimodal Personalized Human-Robot Interaction and the Future of Work and Care

As AI becomes ubiquitous, its physical embodiment—robots–will also gradually enter our lives. As they do, we will demand that they understand us, predict our needs and wants, and adapt to us as we change our moods and minds, learn, grow, and age. The nexus created by recent major advances in machine learning for machine perception, navigation, and natural language processing has enabled human-robot interaction in real-world contexts, just as the need for human services continues to grow, from elder care to nursing to education and training. This talk will discuss our research in socially assistive robotics (SAR), which uses embodied social interaction to support user goals in health, wellness, training, and education. SAR brings together machine learning for user modeling, multimodal behavioral signal processing, and affective computing to enable robots to understand, interact, and adapt to users’ specific and ever-changing needs. The talk will cover methods and challenges of using multi-modal interaction data and expressive robot behavior to monitor, coach, motivate, and support a wide variety of user populations and use cases. We will cover insights from work with users across the age span (infants, children, adults, elderly), ability span (typically developing, autism, stroke, Alzheimer’s), contexts (schools, therapy centers, homes), and deployment durations (up to 6 months), as well as commercial implications.