Riemannian geometry, motion optimization, machine learning, control--these are all buzzwords of my research. I've been developing fast motion optimizers, such as CHOMP and RieMO, that leverage the geometry of differentiable maps as a fundamental building block. The following two videos demo some of this technology:
(See here for more videos.)
My background: I'm now the CEO of Lula Robotics, spinning off technology from the Max Planck Institute for Intelligent Systems in Germany. Before that, I earned my PhD from Carnegie Mellon's Robotics Institute in 2009 studying imitation learning, structured prediction, and functional gradient techniques for learning and optimization. Drew Bagnell, Martin Zinkevich, and I developed a methodology for training planners and control algorithms for robotics (Inverse Optimal Control (IOC)) using ideas from Maximum Margin Structured Classification (MMSC). Our framework is known as Maximum Margin Planning (MMP); we developed a family of online, batch, and functional subgradient methods (exponentiated boosting), collectively known as LEArning to seaRCH to learn efficiently within the framework. Applications include footstep prediction, grasp prediction, heuristic learning, overhead navigation, LADAR classification, and optical character recognition. See the thesis research link to the left to learn more.
Before Germany, I was at TTI-C on the University of Chicago Campus building robots, Intel Labs in both Seattle and Pittsburgh studying trajectory optimization, and Google developing large scale learning systems to assess the quality of Ad Landing Pages.
Here's another video: