Robots are much more than mathematical objects with rigid body structures. Mathematical analysis and computational approximation is highly constraining and has resulted in a vexing divergence between theory and practice. I spent two years studying the geometry and control of robot movement with Stefan Schaal and Marc Toussaint at the Max Planck Institute for Intelligent Systems and the University of Stuttgart. My takeaway? Optimization, control, and machine learning, when combined, can bridge theoretical and real-world robotics in ways never thought possible. I've started a company called Lula Robotics with Jan Issac and Daniel Kappler from Max Planck develop this technology. Contact me if you're interested in learning more.
Riemannian geometry, motion optimization, machine learning, control--these are all buzzwords of my research. I've been working on fast motion optimizers (CHOMP) and faster motion optimizers (RieMO) leveraging the geometry of differentiable maps as a fundamental building block, and even learning some of them for control. Here are a couple demos of what our optimizers can do:
(See here for more videos.)
My background: 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 going to 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: