Mon 03/04
Tobia Marcucci headshot

Seminar @ Cornell Tech: ECE Candidate, Tobia Marcucci

Graphs of convex sets and their applications in robotics and control

In this talk, Tobia Marcucci introduces a novel modeling and computational framework for joint discrete and continuous decision-making. Marcucci considers graphs where each vertex is associated with a convex optimization problem, and each edge couples two problems through additional convex costs and constraints. Marcucci calls these Graphs of Convex Sets (GCS). Many classical problems in graph theory are naturally generalized to GCS, yielding a new class of problems at the interface of combinatorial and convex optimization with a wide variety of applications. For the solution of these problems, Marcucci presents a unified technique that leverages perspective operators to formulate tight convex relaxations and strong mixed-integer formulations. In the second part of the presentation, Marcucci will focus on the shortest-path problem in GCS, and its applications in robot motion planning and optimal control. Marcucci will show that, in these two areas, his optimization techniques generalize or significantly improve upon algorithms that have been developed for decades and are widely used in academia and industry.

Speaker Bio

Tobia Marcucci is a PhD student in Computer Science at the Massachusetts Institute of Technology (MIT), under the supervision of Russ Tedrake and Pablo Parrilo. During his PhD, Tobia has also spent one year at Stanford University as a graduate visiting researcher in Stephen Boyd’s group. Before MIT, Tobia was at the University of Pisa, where he graduated cum laude in mechanical engineering and where he started a PhD in robotics at the Research Center E. Piaggio and the Italian Institute of Technology (IIT). His research lies at the intersection of convex and combinatorial optimization, with applications to robotics, motion planning, and optimal control.