Locations Week 1 (June 12-15):
M, W—Howey (Physics) Building, Room L3 
T—TSRB, Room 132
Th—TSRB, Auditorium
Time: M–Th—1:00-3:00 p.m.

Locations Week 2 (June 19-22):
M, W—Howey (Physics) Building, Room L3 
T—Howey (Physics) Building, Room L5
Th—Marcus (Nano) Building, Room 1116
Time: M–Th—1:00-3:00 p.m.

Course Details

Mini Course: Stochastic Methods for Robotics

Instructor: Gregory S. Chirikjian, Johns Hopkins University
Reading Materials: Slides for lectures will be distributed. Additionally, two books by the instructor may be consulted for greater detail:

• “Stochastic Models, Information Theory, and Lie Groups, Vols. 1+2'' Birkhauser, 2009, 2012
  (Available as PDF from the Georgia Tech Library).
• “Harmonic Analysis for Engineers and Applied Scientists'', Dover, 2016
  (Available from Amazon for ~$25.) 

Each lecture is a two-hour session with a 10-minute break in the middle.

Week 1 (June 12-15)

Lecture 1 (June 12):

• Introduction and Overview of Problem Domains

• Fourier Analysis on the Real Line and on the Circle

• The Discrete Fourier Transform (DFT) and FFT

• Applications to Template Matching

Lecture 2 (June 13):

• Basics of Probability, Statistics and Information Theory

• Properties of Gaussian Distributions and their Generalizations

• Definitions and Concepts of Information Theory

• Applications to Classical State Estimation

Lecture 3 (June 14):

• Numerical Methods for Sampling from a Given Distribution

• Stochastic Differential Equations in Euclidean Space

• Fokker-Planck Equations

• Applications to Particle Filtering

Lecture 4 (June 15):

• Kinematics

• Geometry of Curves, Surfaces and Manifolds

• Stochastic Differential Equations on Curved Spaces

• Applications in Robotics

Week 2 (June 19-22)

Lecture 5 (June 19):

• What is a Lie Group? 

• Fourier Analysis on Groups

• Gaussian Distributions on Groups

• Applications to Hand-Eye Calibration

Lecture 6 (June 20):

• Applications in Mobile Robotics (Parameterizing the Banana Distribution) 

• Applications to Manipulators (Workspace Density and Path of Probability)

• Applications in Kinematic Needle Steering

•  Orientation Estimation and Filtering

Lecture 7 (June 21):

• Some Robot Motion Planning Problems

• Integral Geometry and Geometric Probability

• Minkowski Sums of Ellipsoids

• Parts Entropy and the Design of Self-Replicating Robots

Lecture 8 (June 22):

• Global Optimality in Variation Calculus

• Optimal Curve Reparameterization for Snake Robots

• Applications in Action Recognition

• Applications in Molecular Biophysics

Instructor Bio

Gregory S. Chirikjian received undergraduate degrees from Johns Hopkins University in 1988, and a Ph.D. degree from the California Institute of Technology, Pasadena, in 1992. 

Since 1992, he has served on the faculty of the Department of Mechanical Engineering at Johns Hopkins University, attaining the rank of full professor in 2001. Additionally, from 2004-2007, he served as department chair. 

Chirikjian’s research interests include robotics, applications of group theory in a variety of engineering disciplines, and the mechanics of biological macromolecules. 

He is a 1993 National Science Foundation Young Investigator, a 1994 Presidential Faculty Fellow, and a 1996 recipient of the ASME Pi Tau Sigma Gold Medal. 

In 2008, Chirikjian became a fellow of the ASME, and in 2010, he became a fellow of the IEEE. From 2014-15, he served as a program director for the National Robotics Initiative, which included responsibilities in the Robust Intelligence cluster in the Information and Intelligent Systems Division of CISE at NSF. 

Chirikjian is the author of more than 250 journal and conference papers and the primary author of three books, including Engineering Applications of Noncommutative Harmonic Analysis (2001) and Stochastic Models, Information Theory, and Lie Groups, Vols. 1+2. (2009, 2011). In 2016, an expanded edition of his 2001 book was published as a Dover book under a new title, Harmonic Analysis for Engineers and Applied Scientists.