Winter Term 13/14
HumanOriented Robotics
The course will introduce basic and advanced concepts and methods in robotics, machine learning, artificial intelligence and humanrobot 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, kNN, crossvalidation
 Unsupervised learning: hierarchical clustering, kmeans, 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 HumanRobot 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 opensource alternative to Matlab, can also be used (download for free from www.octave.org).
People
Organization
 Lectures: Monday 14h16h, Room SR 00034, Building 051
 Exercises: Wednesday 12h14h, Room SR 00031, Building 051
 Exams: March 14/26, 2014, Building 074, Office 015
 Language: English
 Recordings: I want you to participate :) so no recordings
Requirements
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, kNN, crossvalidation
 Exercise 7, kMeans clustering
 Exercise 8, Hidden Markov model
 Exercise 9, Kalman filter
 Exercise 10, Tracking and data association
 Exercise 11, Rapidlyexploring 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) 

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
(66 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
(76 slides) 

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

11. 
Temporal Reasoning 3/3: Tracking and Data Association
(84 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!

