Seminars, Minicourses & Lectures

IRIM Seminar Series

COMING FALL 2020 | IRIM Virtual Panel Discussions

PLEASE NOTE: DUE TO THE CURRENT COVID-19 CONDITIONS, IRIM DOES NOT PLAN TO HOST IN-PERSON SEMINARS IN FALL OF 2020.

 

The Institute for Robotics and Intelligent Machines (IRIM) hosts a weekly seminar series featuring guest speakers from around the world with varying backgrounds in robotics and automation. Seminars are held on Wednesdays at 12:15 p.m. in the Marcus Nanotechnology Building in rooms 1116–1118 and alternatively in other auditoriums. ***Please consult the weekly list for location details.***

Two of IRIM's affiliated centers also host weekly seminars. The Machine Learning Center holds seminars on Wednesdays at 12:15 p.m., alternating weekly with IRIM's schedule. The Decision and Control Laboratory (DCL) typically holds seminars on Fridays at 11 a.m.

IRIM's seminar series is video recorded and housed in Georgia Tech Library's SMARTech repository. Additionally, more than five years of historical information about the seminar series is available online.

 


Visiting Faculty Fellows

IRIM’s Visiting Faculty Fellows program supports extended visits (one to six months) to the Georgia Tech Atlanta campus by faculty members from other institutions or industry/government laboratories who are engaged in research activities focusing on robotics. IRIM provides Visiting Fellows with partial salary support, along with support for travel and living expenses. Visiting Fellows interact with IRIM faculty and students and teach a minicourse on their current research during their stay at Georgia Tech.

Current and Past Fellows

2019

Computer Vision: Looking Back to Look Forward | IRIM Robotics Mini-Course Lecture Videos
Svetlana Lazebnik - Associate Professor; Department of Computer Science, University of Illinois at Urbana-Champaign

These days, established computer vision professors are given to complaining, with varying degrees of seriousness, that current Ph.D. students do not know any work in the field that pre-dates the “deep learning revolution” of 2012. However, while wholesale amnesia is unquestionably dangerous for the field, from a pragmatic point of view, even the “old guard” concedes that it is no longer necessary to teach historic work that was truly an intellectual dead end. This short course is an attempt to grapple with the question of what “classical” computer vision techniques should be considered a “must know” for researchers entering the field today, and how past trends and approaches should inform the field as it looks poised to enter a challenging phase—continuing its spurt of rapid growth even while the initial momentum from the “deep learning revolution” begins to fade and negative societal impacts of some maturing technologies come into view.

Introduction and Historical Overview

Connections to Cognitive Science and Psychophysics

History of Ideas in Recognition: Part I

History of Ideas in Recognition: Part II

Future Trends

Ethical and Societal Impacts of Computer Vision Technologies

 

2018

Nonlinear Control for Robots
Mark W. Spong - Professor of Systems Engineering, Professor of Electrical and Computer Engineering, and Excellence in Education Chair in the Erik Jonsson School of Engineering and Computer Science
The University of Texas at Dallas

Mark W. Spong received the Doctor of Science degree in systems science and mathematics in 1981 from Washington University in St. Louis. He has held faculty positions at Lehigh University, Cornell University, and at the University of Illinois at Urbana-Champaign. Currently, he is a professor of Systems Engineering, professor of Electrical and Computer Engineering and holder of the Excellence in Education Chair in the Erik Jonsson School of Engineering and Computer Science at the University of Texas at Dallas. He was Dean of the Jonsson School at UT Dallas from 2008-2017. During his tenure as dean he added four departments of engineering, nine new degree programs, and more than doubled the number of students and faculty.

Review Dynamics of Robot, Feedback Linearization, I/O Linearization and Zero Dynamics

Control of Underactuated Robots I

Control of Underactuated Robots II

Control of Underactuated Robots III, Control of Nonholonomic Systems I

Control of Nonholonomic Systems II

Control of Nonholonomic Systems III

2017

Stochastic Methods for Robotics
Gregory S. Chirikjian - Professor; Department of Mechanical Engineering, Johns Hopkins

Chirikjian’s research interests lie in robotics, automation and manufacturing; biomolecular mechanics, conformational analysis and nanoscience; mathematical crystallography; medical image registration, fiducial design and reconstruction; and in mathematical modeling and computational mathematics. He has developed numerical and analytical techniques for efficient computation of motion in binary robot arm design. He holds four patents for his work.

Lecture 1: Stochastic Methods for Robotics

Lecture 2: Stochastic Methods for Robotics

Lecture 3: Stochastic Methods for Robotics

Lecture 4: Stochastic Methods for Robotics

Lecture 5: Stochastic Methods for Robotics

Lecture 6: Stochastic Methods for Robotics

Lecture 7: Stochastic Methods for Robotics

Lecture 8: Stochastic Methods for Robotics

“Life as a Professor” Video Series