Winter Term 14/15
HumanOriented Robotics
The course will introduce basic and advanced concepts and methods in robotics, machine learning, artificial intelligence and humanrobot interaction. In particular, we will consider robotics with the "human in the loop" which is concerned with key technologies that enable robots to better live, work and interact with humans. This is a growing research field with many new and exciting applications such as companion robots, healthcare robots, autonomous cars, humanrobot cooperation in manufacturing, search and rescue etc.
In addition to their theoretical treatment, the methods will be exemplified with Matlab 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, kNN, crossvalidation
 Unsupervised learning: hierarchical clustering, kmeans, GMM and EM
 Temporal Reasoning: Hidden Markov Models, LDS and Kalman filtering, error propagation
 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 HumanRobot Interaction
We assume that students have a laptop with Matlab installed (download and install instructions can be found here). Octave, the opensource alternative to Matlab, can also be used (download for free from www.octave.org) but some exercises will require Matlabonly functionalities.
People
Organization
 Lectures: Tuesday 10h12h, Building 101, Room SR 01 018
 Exercises: Thursday 12h14h, Building 101, Room SR 01 018
 Exams: Tuesday, March 3, 2015 and Friday, March 27, 2015.
Building 074, Office 015. Enter the building by the parking lot, not the Uni Radio.
 Language: English
 Recordings: I want you to participate :) so no recordings
Requirements
Exercises
 Solving and submitting the exercise sheets does influence the exam grade.
 There are no exam admission requirements.
 In general, assignments will be published on Tuesday and have to be submitted the following Tuesday before class.
 Submit programming exercises via email to the address indicated on the respective sheet.
Exercise Sheets
 Exercise 1, Matlab/Octave basics: sheet, data
 Exercise 2, Introduction to probability: sheet, data
 Exercise 3, Basics of probabilistic reasoning: sheet
 Exercise 4, Naive Bayes classifier: sheet, data
 Exercise 5, Support vector machines: sheet, data
 Exercise 6, kNN, crossvalidation: sheet, data
 Exercise 7, kMeans clustering: sheet, data
 Exercise 8, Hidden Markov model: sheet, data
 Exercise 9, Kalman filter: sheet, data
 Exercise 10, Tracking and data association: sheet, data
 Exercise 11, Rapidlyexploring random trees: sheet, data
 All solutions
Slides and Additional Material
Nr. 
Title 
Additional Material 
1. 
Introduction
(20 slides) 

2. 
Matlab/Octave Tutorial
(121 slides) 

3. 
Probability Refresher
(54 slides) 

4. 
Basics of Probabilistic Reasoning
(38 slides) 
5. 
Supervised Learning 1/3:
Basics, Naive Bayes, logistic regression
(48 slides) 

6. 
Supervised Learning 2/3: Support Vector Machines
(67 slides) 

7. 
Supervised Learning 3/3: AdaBoost, kNN, crossvalidation
(82 slides) 

8. 
Unsupervised Learning: AHC, kmeans, GMMs and EM
(70 slides) 
 Kmeans and Kmedoids 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, AddisonWesley, 2005
 Pattern Recognition by S. Theodoridis, K. Koutroumbas, 4th ed., Elsevier, 2009
 Data clustering: 50 years beyond Kmeans by A.K. Jain, Pat. Rec. Letters, 31(8), 2010

9. 
Temporal Reasoning 1/3: Basics, HMM
(77 slides) 

10. 
Temporal Reasoning 2/3: LDS, Kalman filter
(101 slides) 

11. 
Temporal Reasoning 3/3: Tracking and Data Association
(83 slides) 

12. 
Robot Motion Planning: Basics, combinatorial and samplingbased 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!

