Seminars, Minicourses & Lectures

 

IRIM Seminar Series
All Seminar Sessions Occur @ 12:15 - 1:15 (EST)

Session 6 | Robot Learning: Quo Vadis?

Jan Peters Ph.D. - Professor; Computer Science Department of the Technische
Universitaet Darmstadt | Senior Research Scientist; Max-Planck Institute for
Intelligent Systems

November 18, 2020 | Access URL https://tinyurl.com/IRIMVSSFall6

Abstract: Autonomous robots that can assist humans in situations of daily life have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. A first step towards this goal is to create robots that can learn tasks triggered by environmental context or higher level instruction. However, learning techniques have yet to live up to this promise as only few methods manage to scale to high-dimensional manipulator or humanoid robots. In this talk, we investigate a general framework suitable for learning motor skills in robotics which is based on the principles behind many analytical robotics approaches. It involves generating a representation of motor skills by parameterized motor primitive policies acting as building blocks of movement generation, and a learned task execution module that transforms these movements into motor commands. We discuss learning on three different levels of abstraction, i.e., learning for accurate control is needed to execute, learning of motor primitives is needed to acquire simple movements, and learning of the task-dependent „hyperparameters“ of these motor primitives allows learning complex tasks. We discuss task-appropriate learning approaches for imitation learning, model learning and reinforcement learning for robots with many degrees of freedom. Empirical evaluations on a several robot systems illustrate the effectiveness and applicability to learning control on an anthropomorphic robot arm. These robot motor skills range from toy examples (e.g., paddling a ball, ball-in-a-cup) to playing robot table tennis against a human being and manipulation of various objects.
 
Bio: Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt and at the same time an adjunct senior research scientist at the Max-Planck Institute for Intelligent Systems, where he heads the interdepartmental Robot Learning Group between the departments of Empirical Inference and Autonomous Motion. Jan Peters has received a few awards, most notably, he has received the Dick Volz Best US PhD Thesis Runner Up Award, the Robotics: Science & Systems - Early Career Spotlight, the IEEE Robotics & Automation Society's Early Career Award, and the International Neural Networks Society's Young Investigator Award.
 
Access the Seminar Here:
https://tinyurl.com/IRIMVSSFall6

 

 

 
Special Event in AI  & Ethics
Ethical Management of AI: A French-American Dialogue
November 9 & 10, 2020 | Virtual Event
 

Please join Professors Ayanna Howard (GT-ECE) & Jason Borenstein (GT-PubPolicy) and other academic colleagues, along with  the Consulate General of France in Atlanta, for a free two day virtual event taking place on November 9-10th.  The event focuses on Artificial Intelligence (AI) and how AI is connected to privacy, trust, and human oversight in health and well-being.  The event is organized by several partners, including Georgia Tech's Ethics, Technology, and Human Interaction Center (ETHICx). 

For more information, https://tinyurl.com/EthicalAI2020



Visiting Faculty Fellows Mini-Courses

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.

 IRIM Fellows Emeritus

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

Affiliated Center Seminars

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.