Social Robotics Lab, University of Freiburg

// 1. Include Header

Faculty of Engineering > Department of Computer Science >

Winter Term 13/14

Human-Oriented Robotics

The course will introduce basic and advanced concepts and methods in robotics, machine learning, artificial intelligence and human-robot interaction. While the methods are of general interest for students interested in advanced robotics, we will also discuss techniques that consider the "human in the loop" – a growing field of activity in robotics with new exciting applications. The challenge is to develop key technologies that enable robots to better live, work and interact with humans. In addition to their theoretical treatment, the methods will be exemplified with programming exercises. The course outline is as follows:

Introduction
Basics

Matlab/Octave introduction
Probability refresher, common distributions
Probabilistic reasoning, Bayes networks, Markov chains

Perception of Humans

Supervised learning: naive Bayes, logistic regression, SVM, AdaBoost, k-NN, cross-validation
Unsupervised learning: hierarchical clustering, k-means, GMM and EM
Temporal Reasoning: Hidden Markov Models, LDS and Kalman filtering: representation and inference
Tracking and data association: NN, GNN, PDAF, JPDAF, MHT

Planning among Humans

Robot motion planning: combinatorial techniques, PRM, RRT, potential fields, A*, Theta*, obstacle avoidance
From plans to policies: Markov Decision Processes (MDP)

Interaction with Humans

Introduction to Human-Robot Interaction

The course is accompanied by programming exercises in Matlab. We assume that students have a laptop with Matlab installed (download and install instructions can be found here). Octave, the open-source alternative to Matlab, can also be used (download for free from www.octave.org).

People

Lecturer: Prof. Kai Arras
Lab instructors: Timm Linder, Luigi Palmieri, Billy Okal

Organization

Lectures: Monday 14h-16h, Room SR 00-034, Building 051
Exercises: Wednesday 12h-14h, Room SR 00-031, Building 051
Exams: March 14/26, 2014, Building 074, Office 015
Language: English
Recordings: I want you to participate :-) so no recordings

Requirements

The course Introduction to Mobile Robotics, held during the summer term, is recommended. It is not a formal requirement.

Exercises

Solving and submitting the exercise sheets is not mandatory to be admitted to the final exam. Of course, it is highly recommended for the sake of a successful exam.
There are no bonus points, no exam admission requirements.
In general, assignments will be published on Monday and have to be submitted the following Monday before class.
Submit programming exercises via email to the address indicated on the respective sheet.

Exercise Sheets

Exercise 1, Matlab/Octave basics
Exercise 2, Introduction to probability
Exercise 3, Basics of probabilistic reasoning
Exercise 4, Naive Bayes classifier
Exercise 5, Support vector machines
Exercise 6, k-NN, cross-validation
Exercise 7, k-Means clustering
Exercise 8, Hidden Markov model
Exercise 9, Kalman filter
Exercise 10, Tracking and data association
Exercise 11, Rapidly-exploring random trees
Exercise 12, Q&A session

Slides and Additional Material

Nr.	Title	Additional Material
1.	Introduction (19 slides)
2.	Matlab/Octave Tutorial (121 slides)	Matlab online documentation Octave user manual (pdf) Install Matlab from here (University of Freiburg only) SRL librobotics library
3.	Probability Refresher (54 slides)	Pattern Recognition and Machine Learning by C.M. Bischop, Springer, 2007 Probabilistic graphical models: principles and techniques by D. Koller, N. Friedman Probability, Random Variables and Stochastic Processes by A. Papoulis, S.U. Pillai Computer vision: models, learning and inference by S.J.D. Prince
4.	Basics of Probabilistic Reasoning (38 slides)
5.	Supervised Learning 1/3: Basics, Naive Bayes, logistic regression (48 slides)	Artificial Intelligence: A Modern Approach by S. Russell, P. Norvig Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression by T. M. Mitchel, in: Machine Learning, Chapter 1, 2010. Estimating continuous distributions in Bayesian classifiers by G. John, P. Langley, UAI 1995.
6.	Supervised Learning 2/3: Support Vector Machines (66 slides)	SVM Applet by A. Karpathy. Very instructive! Part V: Support Vector Machines by A. Ng, Lecture Notes CS229 Machine Learning, Stanford University, 2012 Support Vector Machines Explained by T. Fletcher, Tutorial Paper, 2009
7.	Supervised Learning 3/3: AdaBoost, k-NN, cross-validation (82 slides)	K-NN and Potential Energy Applet by E.M. Mirkes. Very instructive! Introduction to Machine Learning by E. Alpaydin, The MIT Press, 2009 Debugging Learning Algorithms by A. Ng, A lecture on getting machine learning algorithms to work in practice, 2007
8.	Unsupervised Learning: AHC, k-means, GMMs and EM (70 slides)	K-means and K-medoids Applet by E.M. Mirkes. Very instructive! EM for Mixture Models Applet by I. Dinov. Very instructive! Introduction to Data Mining by P.-N. Tan, M. Steinbach, V. Kumar, Addison-Wesley, 2005 Pattern Recognition by S. Theodoridis, K. Koutroumbas, 4th ed., Elsevier, 2009 Data clustering: 50 years beyond K-means by A.K. Jain, Pat. Rec. Letters, 31(8), 2010
9.	Temporal Reasoning 1/3: Basics, HMM (76 slides)	A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition by L.R. Rabiner, Proc. of the IEEE, 77(2), 1989 mathematicalmonk YouTube channel, Machine learning lectures 14.4-14.12 on HMM
10.	Temporal Reasoning 2/3: LDS, Kalman filter (81 slides)	Estimation with Applications to Tracking and Navigation by Y. Bar-Shalom, X. Rong Li, T. Kirubarajan, Wiley, 2001 Probabilistic Robotics by S. Thrun, W. Burgard, D. Fox, MIT Press, 2005
11.	Temporal Reasoning 3/3: Tracking and Data Association (84 slides)	Tracking and Data Fusion: A Handbook of Algorithms by Y. Bar-Shalom, P.K. Willett, X. Tian, YBS Publishing, 2011 Beyond the Kalman Filter: Particle Filters for Tracking Applications by B. Ristic, S. Arulampalam, N. Gordon, Artech House, 2004
12.	Robot Motion Planning: Basics, combinatorial and sampling-based methods (67 slides)	Robot Motion Planning by J.C. Latombe, Kluwer Academic Publishers, 1991 Planning Algorithms by S. LaValle, Cambridge University Press, 2006 Principles of Robot Motion: Theory, Algorithms, and Implementations by H. Choset, K.M. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L.E. Kavraki, S. Thrun, MIT Press, 2005 Chapters 5 and 6 in Handbook of Robotics ed. by B. Siciliano, O. Khatib, Springer, 2008 Configuration Space Applet by K. Goldberg, E. Lee, J. Wiegley. Very instructive!