How AI helps robots see our world | John Leonard | TEDxMIT

We are developing tools to enable physically-embodied, spatial AI as a "technological superpower" for robots and people. Semantic simultaneous localization and mapping can enable long-lived autonomous systems to navigate in complex dynamic environments. Our dream is to create mobile robots that can build and maintain models of the world through lifelong learning, improving their performance over time, and helping humans to perform difficult tasks.
AI, Machine Learning, Oceans, Robots John J. Leonard is Samuel C. Collins Professor of Mechanical and Ocean Engineering and Associate Department Head for Education in the MIT Department of Mechanical Engineering. He is also a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His research addresses the problems of navigation and mapping for autonomous underwater vehicles, self-driving vehicles, and other types of mobile robots. He holds the degrees of B.S.E.E. in Electrical Engineering and Science from the University of Pennsylvania (1987) and D.Phil. in Engineering Science from the University of Oxford (1994). He is an IEEE Fellow (2014) and an AAAS Fellow (2020). Prof. Leonard is a Technical Advisor at Toyota Research Institute.
John J. Leonard is Samuel C. Collins Professor of Mechanical and Ocean Engineering and Associate Department Head for Education in the MIT Department of Mechanical Engineering. He is also a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His research addresses the problems of navigation and mapping for autonomous underwater vehicles, self-driving vehicles, and other types of mobile robots. He holds the degrees of B.S.E.E. in Electrical Engineering and Science from the University of Pennsylvania (1987) and D.Phil. in Engineering Science from the University of Oxford (1994). He is an IEEE Fellow (2014) and an AAAS Fellow (2020). Prof. Leonard is a Technical Advisor at Toyota Research Institute. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at www.ted.com/tedx