The goal of Human-Centered Embodied Intelligence is to develop robots
that can work safely alongside humans, helping them to to perform
difficult or dangerous tasks. Two key themes are increasing the
expressive capacity of the environmental models used in localization
and mapping systems (representation) and improving the performance of
the algorithms used to estimate these models from data (inference).
Our ultimate goal is to provide autonomous robots with a more
comprehensive understanding of the world, facilitating life-long
learning in complex dynamic environments. In this talk, we will
discuss a variety of research projects spanning automated driving,
object-based mapping and localization, and autonomous underwater
vehicle navigation. In particular, we will describe
certifiably-correct range-aided SLAM (Papalia et al., IEEE TRO 2024),
a new approach for range-aided simultaneous localization and mapping
problems that computes certifiably optimal solutions.
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.