Uploaded March 2024 | Updated September 2026, 2 hours ago
Microsoft Future Leaders in Robotics and AI Seminar Series: Think graphical, act local: Distributed inference for multi-robot coordination
Online Seminar
Jana Pavlasek
PhD Candidate
University of Michigan
To achieve the long-promised general robot assistants capable of operating in our uncertain and unstructured world, we need efficient, reliable, and cooperative AI systems. My research explores advancements in distributed methods for efficient, adaptive, and robust inference for robotic perception and planning problems. Distributed inference leverages graphical models to break up complex problems into multiple smaller subproblems which can be solved in parallel. In this talk, I will present my work on distributed inference for challenging robotic problems including robot perception and multi-robot coordination. I will demonstrate how nonparametric representations of uncertainty help tackle high-dimensional problems with multiple diverse solutions efficiently. First, I will present my contributions to nonparametric belief propagation for the problem of articulated object localization in cluttered scenes towards robot manipulation of hand-tools. Next, I will show how these ideas are extended to distributed planning over multi-robot teams through a new algorithm called Stein Variational Belief Propagation.
For more information on the Maryland Robotics Center see:
https://robotics.umd.edu
Microsoft Future Leaders in Robotics and AI Seminar Series: Think graphical, act local: Distributed inference for multi-robot coordination
Online Seminar
Jana Pavlasek
PhD Candidate
University of Michigan
To achieve the long-promised general robot assistants capable of operating in our uncertain and unstructured world, we need efficient, reliable, and cooperative AI systems. My research explores advancements in distributed methods for efficient, adaptive, and robust inference for robotic perception and planning problems. Distributed inference leverages graphical models to break up complex problems into multiple smaller subproblems which can be solved in parallel. In this talk, I will present my work on distributed inference for challenging robotic problems including robot perception and multi-robot coordination. I will demonstrate how nonparametric representations of uncertainty help tackle high-dimensional problems with multiple diverse solutions efficiently. First, I will present my contributions to nonparametric belief propagation for the problem of articulated object localization in cluttered scenes towards robot manipulation of hand-tools. Next, I will show how these ideas are extended to distributed planning over multi-robot teams through a new algorithm called Stein Variational Belief Propagation.
For more information on the Maryland Robotics Center see:
https://robotics.umd.edu
