SUMMARY

As robots and AI systems become more prevalent in every-day life, humans and machines will have to work closely together. Robotic devices will be used to support human health, service robots will operate alongside humans in homes, and autonomous vehicles will have to safely drive end-users to their destination. Yet, humans exhibit a high degree of heterogeneity which poses a challenge for robotic systems that are tasked with learning from and supporting humans. For example, in a medical setting, individual patients are likely to have different needs and varying biology that must be accounted for. Autonomous Vehicles (AVs) will have to learn about the differing preferences of end-users and adapt accordingly. Because of this human heterogeneity, one-size-fits-all algorithms will not suffice in many human-machine interaction scenarios. Instead, to effectively support humans, machines must be capable of recognizing individual desires, abilities, and characteristics and adapt to account for differences across individuals. This thesis focuses on the development of personalized algorithms that enable machines to better support and work with humans. Specifically, I aim to develop and research novel techniques for safely and efficiently supporting heterogeneous humans across various robotic domains. In this work, I develop data-driven, personalized frameworks in healthcare, learning from demonstration, and autonomous driving domains to account for heterogeneity amongst end-users.