Abstract

Uncertainty propagation and mitigation is at the core of all robotic and control systems. The standard approach so far has followed the spirit of control of a system “with uncertainties,” as opposed to direct control “of uncertainties.” Recent advances from the controllability of higher order moments of the distribution of the state trajectories provide us with a new tool to control stochastic systems with strict performance guarantees. In this talk I will review some recent results on covariance and distribution control for discrete stochastic systems, subject to probabilistic (chance) constraints, and will demonstrate the application of the approach on control and robot motion planning problems under uncertainty. The resulting theory has close connections to the classical Optimal Mass Transport (OMT) problem and is numerically efficient (often resulting in a convex program). I will also discuss some current trends and potential directions for future work.

The seminar will be held in the Board Room, Baker Building, Department of Engineering, and online (zoom): https://newnham.zoom.us/j/92544958528?pwd=YS9PcGRnbXBOcStBdStNb3E0SHN1UT09