  In order to operate in and interact with the physical world, robots need to
have estimates of the current and future state of the environment. We thus
equip robots with sensors and build models and algorithms that, given some
measurements, produce estimates of the current or future states. Environments
can be unpredictable and sensors are not perfect. Therefore, it is important to
both use all information available, and to do so optimally: making sure that we
get the best possible answer from the amount of information we have. However,
in prevalent research, uncommon sensors, such as sound or radio-frequency
signals, are commonly ignored for state estimation; and the most popular
solvers employed to produce state estimates are only of local nature, meaning
they may produce suboptimal estimates for the typically non-convex estimation
problems. My research aims to use resources more optimally, by building on 1)
multi-modality: using ubiquitous RF transceivers and microphones to support
state estimation, 2) building certifiably optimal solvers and 3) learning and
improving adequate models from data.
