Learning Socially Assistive Robot Behaviors For Personalized Human Robot Interaction PDF Download

Cognitively assistive robots have great potential to improve the accessibility of healthcare services by extending existing clinical interventions to a person's home. This provides a variety of benefits, including extending the reach of professional services, allowing people to engage with these interventions at their own convenience, and reducing risk of exposure to illness at clinics. However, there are many obstacles to deploying these robots longitudinally and autonomously, particularly for populations with lower technology literacy such as older adults. These obstacles include enabling robots to leverage the expert domain knowledge of clinicians and other stakeholders, contextualizing the robot and intervention to the lives of users, and understanding and adapting to a person's intervention preferences and goals. The goal of my work is to design robots that can continuously learn from and adapt to people in real-world environments, which I explore in the context of delivering neurorehabilitation to people with cognitive impairments. In this dissertation, I will describe three main contributions of my work. First, I developed new methods to recognize complex motion reflective of real-world activities to enable robots to accurately understand human intention. Recognizing human activity can help robots understand a person's state and their reactions to its behavior. My work revealed the complementary strengths of two common sensor modalities for recognizing gross and fine motion, which can be leveraged to recognize complex activities and help robots better understand human intention. In addition, I designed a novel deep learning architecture for recognizing fine motion using nonvisual sensors, enabling robots to recognize human activity in dynamic, privacy sensitive settings such as homes. Second, I developed the first robotic system (JESSIE) which makes control synthesis accessible to novice programmers, allowing clinicians to quickly and easily specify complex robot behaviors through a tangible specification interface. Clinicians can provide robots with valuable domain and personal knowledge which can inform its behavior. My work revealed key insights regarding how robots can learn and adapt to people with cognitive impairments longitudinally at home. JESSIE makes control synthesis more accessible to novice programmers, enabling stakeholders to imbue robots with their domain knowledge and extend the reach of their work. Third, I developed an autonomous robot (CARMEN) which extends clinical interventions to the home, and longitudinally supports goal progress and motivation. In collaboration with clinicians and people with cognitive impairments, I identified interaction design patterns for translating clinical interventions to robots in order to maintain longitudinal engagement and maximize efficacy. Furthermore, I developed a new framework for roboticists creating longitudinal, robot-delivered health interventions with collaborative goal setting capabilities. My work lays the foundation for enabling robots to support motivation and goal achievement throughout a longitudinal intervention at home. My research contributes to building robotic systems which can longitudinally personalize their behavior to people in real-world environments. My work will transform how robots longitudinally interact with people, with the ultimate goal of enabling more safe and effective human-robot interaction, particularly for underserved populations.