UC San Diego computer scientist Laurel Riek wants to put a robot in someone's home for six months.
"We want to build robots that can adapt to learn from and change with a person, not only throughout the week, but throughout the day," she says in this video for the journal Communications of the ACM.
Riek is the author of a review article titled Healthcare Robotics in the journal's November 2017 issue.
The full text of the article is available here: https://cacm.acm.org/magazines/2017/11/222171-healthcare-robotics/fulltext
She is a professor of computer science at the Jacobs School of Engineering at UC San Diego and a faculty member of the campus' Contextual Robotics Institute.
Her research goal is to enable robots to robustly solve problems in dynamically- changing human environments. Riek is particularly focused on problems in real-world, safety-critical healthcare environments, such as hospitals, homes and clinics. Her work tackles the fundamental and applied problems that make complex, real-world perception and interaction in these spaces so challenging for robots. Riek’s work draws on techniques from the fields of computer vision, machine learning, non-linear dynamics, and human factors to enable robots to autonomously perceive, respond, and adapt to people in the real world.
"We want to build robots that can adapt to learn from and change with a person, not only throughout the week, but throughout the day," she says in this video for the journal Communications of the ACM.
Riek is the author of a review article titled Healthcare Robotics in the journal's November 2017 issue.
The full text of the article is available here: https://cacm.acm.org/magazines/2017/11/222171-healthcare-robotics/fulltext
She is a professor of computer science at the Jacobs School of Engineering at UC San Diego and a faculty member of the campus' Contextual Robotics Institute.
Her research goal is to enable robots to robustly solve problems in dynamically- changing human environments. Riek is particularly focused on problems in real-world, safety-critical healthcare environments, such as hospitals, homes and clinics. Her work tackles the fundamental and applied problems that make complex, real-world perception and interaction in these spaces so challenging for robots. Riek’s work draws on techniques from the fields of computer vision, machine learning, non-linear dynamics, and human factors to enable robots to autonomously perceive, respond, and adapt to people in the real world.