Difference between revisions of "Robotics"

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==Teaching Material (the textbook)==  
 
==Teaching Material (the textbook)==  
  
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Lectures will be based on material taken from the book.  
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Lectures will be based on material from different sources, teachers will provide their slides to students as soon they are available taken from the book.  
  
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* [http://www-bcf.usc.edu/~gareth/ISL/ An Introduction to Statistical Learning with Applications in R] by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
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<!--* [[Media:PAMI2015-01-Intro.pdf | [2015] Course introduction]]: introductory slides of the course with useful information about the grading, and the course logistics. Some examples from supervised and unsupervised learning. Regression, classification, clustering terminology and examples.-->
  
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If you are interested in a more deep treatment of the topics you can refer to the following book from the same authors
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If you are interested in a more deep treatment of the topics presented by the teachers you can refer to the following books and papers:
  
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* [http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html The Elements of Statistical Learning: Data Mining, Inference, and Prediction.] by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.
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* [http://www.probabilistic-robotics.org/ Probabilistic Robotics] by Dieter Fox, Sebastian Thrun, and Wolfram Burgard.
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Some additional material that could be used to prepare the oral examination will be provided together with the past homeworks.
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===Teacher Slides===
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In the following you can find the lecture slides used by the teacher and the teaching assistants during classes.
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Lectures:
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* [[Media:PAMI2015-01-Intro.pdf | [2015] Course introduction]]: introductory slides of the course with useful information about the grading, and the course logistics. Some examples from supervised and unsupervised learning. Regression, classification, clustering terminology and examples.
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* [[Media:PAMI2015-02-StatisticalLearning.pdf | [2015] Statistical Learning Introduction]]: Statistical Learning definition, rationale, and trade-offs (e.g., prediction vs. inference, parametric vs non parametric models, flexibility vs. interpretability, etc.)
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* [[Media:PAMI2015-03-AssessingModelAccuracy.pdf | [2015] Statistical Learning and Model Assessment]]: Model Assessment for Regression and Classification, Bias-Variance trade-off, Model complexity and overfitting, K-Nearest Neighbors Classifier vs. Bayes Classifier.
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* [[Media:PAMI2014-04-LinearRegression.pdf | [2014-2015] Linear Regression]]: Simple Linear Regression and Multiple Linear Regression. Feature selection. Ridge Regression and Lasso.
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* [[Media:PAMI2014-05-LinearClassification.pdf | [2014-2015] Linear Classification]]: From Linear Regression to Logistic Regression. Linear Discriminant Analysis and Quadratic Discriminant Analysis. Comparison between linear classification methods.
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* [[Media:PAMI2014-06-SupportVectorMachines.pdf | [2014-2015] Support Vector Machines]]: Discriminative vs. generative methids. Hyperplanes learning and Perceptron. Maximum Margin Classifiers. The Kernel trick and Support Vector Machines.
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For exercises and lab material please refer to [http://davide.eynard.it/pattern-analysis-and-machine-intelligence-2015-2016/ Davide Eynard website].
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<!--
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http://davide.eynard.it/pattern-analysis-and-machine-intelligence-2015-2016/
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* Lab 1: Introduction to R
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**[[Media:BasicsofR.txt | Basics of R]]: the list of commands ran in Lab 01. Note that the list is heavily based on the Lab in Section 2.3 of the book (you can find the original [http://www-bcf.usc.edu/~gareth/ISL/code.html here]), but I preferred to integrate it with some additional hints from my personal experience and other sources such as [http://www.pitt.edu/~njc23/ this one])
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**[http://www.statlearning.com/ Statistical Learning]: the website of the Introduction to Statistical Learning book. In the [http://www-bcf.usc.edu/~gareth/ISL/data.html Data Sets and Figures] page you will also find links to download the Auto.data and Auto.cvs datasets we used during the Lab.
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**[http://cran.r-project.org/ The Comprehensive R Archive Network]: the place where you can download R and its packages (note that the book often refers to ISLR and MASS packages, it is good for you to install them soon)
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* [[Media:Lab02.pdf | Lab2]]: Questions and exercises on Statistical Learning
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* [[Media:Lab03.pdf | Lab3]]: First exercises on linear regression
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-->
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<!--
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* [[Media:PAMI_Intro.pdf | Course introduction]]: introductory slides of the course with useful information about the grading, and the course logistics. Some examples from supervised learning and two algorithms for classification (taken from ''The Elements of Statistical Learning'' book).
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* [[Media:ProbabilityBasics.pdf | Probability Basics]]: Slides on probability basics used to introduce Statistical Decision Theory.
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* [[Media:PAMI_ModelSelection.pdf | Model Selection]]:  slides presenting images, tables and examples about model selection (taken from ''The Elements of Statistical Learning'' book).
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* [[Media:PAMI_LinearClassification.pdf | Linear Classification Examples]]: slides presenting images, tables and examples about (generalized) linear methods for classification (taken from ''The Elements of Statistical Learning'' book).
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* [[Media:PAMI_KernelSmoothing.pdf | Kernel Smoothing Examples]]: slides presenting images, tables and examples about Kernel Smoothing, Kernel Density Estimation and Gaussian Mixture Models (taken from ''The Elements of Statistical Learning'' book).
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* [[Media:PAMI_DTnR.pdf | Decision Trees and Classification Rules]]: these slides have been used to present decision trees and decision rules complementing the material in Ch. 9.2 of the ''The Elements of Statistical Learning'' book.
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* [[Media:PAMI_SVM.pdf | Support Vector Machines]]: these slides have been used to present Support Vector Machines (taken from ''The Elements of Statistical Learning'' book).
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-->
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===Additional Resources===
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Papers and links useful to integrate the textbook
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* [http://scott.fortmann-roe.com/docs/BiasVariance.html Bias vs. Variance]: "Understanding the Bias-Variance Tradeoff" essay by Scott Fortmann-Roe
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* ...
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<!--
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* Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani, [http://www.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf Least Angle Regression] Annals of Statistics (with discussion) (2004) 32(2), 407-499.
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* Burges, Christopher J. C., 1998. [http://www.svms.org/tutorials/Burges1998.pdf A tutorial on support vector machines for pattern recognition]. Data Mining and Knowledge Discovery, 2(2), 121–167.
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* ...
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-->
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<!--
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===Clustering Slides===
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These are the slides used to present clustering algorithms during lectures
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* Lesson 1: Introduction to Clustering and K-Means ([http://davide.eynard.it/teaching/2012_PAMI/slides-lecture-e1.pdf slides], [http://davide.eynard.it/teaching/2012_PAMI/handout-lecture-e1.pdf handouts])
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* Lesson 2: K-Means alternatives, Hierarchical, SOM ([http://davide.eynard.it/teaching/2012_PAMI/slides-lecture-e2.pdf slides], [http://davide.eynard.it/teaching/2012_PAMI/handout-lecture-e2.pdf handouts])
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* Lesson 3: Mixture of Gaussians, DBSCAN, Jarvis-Patrick ([http://davide.eynard.it/teaching/2012_PAMI/slides-lecture-e3.pdf slides], [http://davide.eynard.it/teaching/2012_PAMI/handout-lecture-e3.pdf handouts])
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* Lesson 4: Evaluation measures ([http://davide.eynard.it/teaching/2012_PAMI/slides-lecture-e4.pdf slides], [http://davide.eynard.it/teaching/2012_PAMI/handout-lecture-e4.pdf handouts]) and Spectral Clustering ([http://davide.eynard.it/teaching/2012_PAMI/Spectral%20Clustering.pdf])
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-->
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===Past Exams and Sample Questions===
 
===Past Exams and Sample Questions===
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Since 2014/2015 the course was changed and the exams format as well. For this edition of the course you should expect '''2 theoretical questions + 2 practical exercises''' (on average). Some examples from the past year can be found here:
 
  
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* [[Media:2015_02_09_PAMI.pdf |09/02/2015 Exam]]
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In the 2015/2016 Academic Year the course has changed significantly and the exam format as well. For this edition of the course we do not have past exams to share with you, should expect '''2 theoretical questions + 2 practical exercises''' (on average), no coding exercise in the exams since you will see some coding already in the home project. During the lectures the teachers will suggest possible exercises you might encounter in the exams.
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* [[Media:2015_02_23_PAMI.pdf |23/02/2015 Exam]]
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* [[Media:2015_06_07_PAMI.pdf |07/06/2015 Exam]]
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* [[Media:2015_09_14_PAMI.pdf |14/09/2015 Exam]]
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* [[Media:2015_09_30_PAMI.pdf |30/09/2015 Exam]]
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These are the text of past exams to give and idea on what to expect a theoretical questions:
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* [[Media:2013_09_20_PAMI.pdf |20/09/2013 Exam]]
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* [[Media:2013_09_10_PAMI.pdf |10/09/2013 Exam]]
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* [[Media:2013_07_26_PAMI.pdf |26/07/2013 Exam]]
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* [[Media:2013_07_11_PAMI.pdf |11/07/2013 Exam]]
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* [[Media:2013_01_29_PAMI.pdf |29/01/2013 Exam]]
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* [[Media:2012_09_19_PAMI.pdf |19/09/2012 Exam]]
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* [[Media:2012_09_04_PAMI.pdf |04/09/2012 Exam]]
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* [[Media:2012_07_10_PAMI.pdf |10/07/2012 Exam]]
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* [[Media:2012_06_26_PAMI.pdf |26/06/2012 Exam]]
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* [[Media:2012_02_03_PAMI.pdf |03/02/2012 Exam]]
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* [[Media:2011_09_19_PAMI.pdf |19/09/2011 Exam]]
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* [[Media:2011_09_08_PAMI.pdf |08/09/2011 Exam]]
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* [[Media:2011_07_15_PAMI.pdf |15/07/2011 Exam]]
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* [[Media:2011_06_29_PAMI.pdf |29/06/2011 Exam]]
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===Online Resources===
 
===Online Resources===
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The following are links to online sources which might be useful to complement the material above
 
The following are links to online sources which might be useful to complement the material above
  
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* [http://math.arizona.edu/~hzhang/math574m.html MATH 574M] University of Arizona Course on ''Statistical Machine Learning and Data Mining''; here you can find slides covering part of the course topics (the reference book for this course is again ''The Elements of Statistical Learning'')
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* [http://www.ros.org/ ROS]: the Robot Operating System
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* [http://gazebosim.org/ Gazebo]: the Gazebo robot simulator
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<!--
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== 2013-2014 Homework ==
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The 2013 Homework (alike the 2012 one) is organized as an octave series of tutorials. You are requested to go through the tutorials and practice with the algorithms that have been presented in class. To prove us you have done it and that you have understood the code you will be requested to solve few exercises and provide us a pdf report by email
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=== Part 1: Linear Classification Methods ===
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* [[Media:homework_pami_classification_2013_2014.pdf | Homework 2013-2014 on Classification]]: this is the text of the first part of the homework; it has been intentionally edited not to allow cut and paste. '''This part of the homework will contribute to the 10% of the grade and the deadline to submit the solution by Sunday 17/11 23:59'''
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** [[Media:SAheart.data | SAheart.data]]: the dataset used for the homework
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** [[Media:SAheart.info | SAheart.info]]: the dataset used for the homework
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'''Note 1:''' Submit the solution by loading it on www.dropitto.me/matteucci (pwd is dropittome)
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'''Note 2:''' please name your pdf as pami_SURNAME_STUDENTID_classification.pdf; if you submit a homework for different people, please pick one of the names for the file but PUT ALL THE NAMES IN THE COVER PAGE!!
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=== Part 2: Regression ===
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* [[Media:homework_pami_regression_2013_2014.pdf | Homework 2013-2014 Regression]]: this is the text of the second part of the homework; it has been intentionally edited not to allow cut and paste. '''This part of the homework will contribute to the 10% of the grade and the deadline to submit the solution by email to malago@di.unimi.it (cc to matteo.matteucci@polimi.it) is Friday 20/12 23:59'''
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** [[Media:prostate.data | prostate.data]]: the dataset used for the homework
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** [[Media:prostate.info | prostate.info]]: the dataset used for the homework
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** [[Media:diabete.mat | diabete.mat]]: the dataset used for the homework
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** [[Media:textread.m | textread.m]]: (optional) function which might be useful depending on your octave version
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** [[Media:strread.m | strread.m]]: (optional) function which might be useful depending on your octave version
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For any question or doubt please sen us an email as soon as possible.
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'''Note 1:''' for some strange reason the CSM of the website has decided to rename the files with capitals, please save them in lower case :-(
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<strike>'''Note 2:''' rename the file Diabete.data into diabete.mat ... still fighting with the CSM :-)</strike>
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'''Note 3:''' the code has been tested with octave under linux, we suggest to use it not to spend too much time with installing it under windows or using matlab. If you do not have linux installed, try using a live CD as the ubuntu 13.04 live distro ;-)
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=== Part 3: Clustering ===
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The code and the text of the third part of the homework are available online at this post
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* [http://davide.eynard.it/2013/12/30/octave-clustering-demo-part-6-more-evaluation/ Homework 2013-2014 on clustering evaluation]
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As usual, '''this part of the homework will contribute to the 10% of the grade'''; the deadline to submit the solution is the end of the course. You have to '''send it to davide.eynard_at_gmail.com Friday 24/01 23:59'''.
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'''Note 1:''' for any doubt or question send an email, as soon as possible, to Davide Eynard so to have a prompt reply and not get stuck during homework execution.
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'''Note 2:''' you have to turn in only the solution of "Ocatave clustering demo part 6", while the other parts can be used as reference to improve your understanding in basic clustering algorithms.
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=== Part 2: Classification ===
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* [[Media:homework_pami_classification_2013.pdf | Homework 2013 Classification]]: this is the text of the second part of the homework; it has been intentionally edited not to allow cut and paste. '''This part of the homework will contribute to the 10% of the grade and the deadline to submit the solution by Sunday 23/06 23:59'''
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** [[Media:SAheart.data | SAheart.data]]: the dataset used for the homework
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** [[Media:SAheart.info | SAheart.info]]: the dataset used for the homework
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'''Note 1:''' Submit the solution by loading it on www.dropitto.me/matteucci (pwd is dropittome)
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'''Note 2:''' please name your pdf as pami_SURNAME_STUDENTID_classification.pdf; if you submit a homework for different people, please pick one of the names for the file but PUT ALL THE NAMES IN THE COVER PAGE!!
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'''Errata Corrige''': there were a few bugs in the homework text. I have updated the pdf and they were:
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In the computation of feature projection, the code for the maximization of a'B*a via SVD should be changed as it follows
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% maximization of a'*B*a / a'*w*a via SVD
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[Vw, Dw, Vw] = svd(W);
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Whalf = Vw * sqrt(Dw) * Vw'; % Whalf'*Whalf == W
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Wminushalf = inv(Whalf);
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Mstar = M*Wminushalf;
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    % Add this variable for computing Mstar mean
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    meanMstar = mean(Mstar);
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for i=1:size(M,1)
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    % Remove the mean saved before the loop
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    Mstar(i,:) = Mstar(i,:)-meanMstar;
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end
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Bstar = Mstar'*Mstar;
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[Vstar, Db, Vstar] = svd(Bstar);
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In the Fisher projection it is more correct to use only the training data to learn the projection and then we can train and test on the corresponding subsets
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a = FisherProjection(X(training,:),Y(training,:));
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reducedX = X*a(:,1);
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[mu_0, mu_1, sigma, p_0, p_1] = linearDiscriminantAnalysis_train(reducedX(training), Y(training))
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I forgot to filter for just the training samples when performing Quadratic Discriminant Analysis
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quadX = expandToQuadraticSpace(X);
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%check this out!
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size(quadX)
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beta = linearRegression_train(quadX(training), Y(training));
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And in general you should always train on the training data and test on the testing data ;-).
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=== Part 3: Clustering ===
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The code and the text of the third part of the homework are available online at these posts
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* [http://davide.eynard.it/2013/06/18/octave-clustering-demo-part-4-k-medoids/ Homework 2013 on k-medoids]
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* [http://davide.eynard.it/2013/06/18/octave-clustering-demo-part-5-hierarchical-clustering/ Homework 2013 on hierarchical clustering]
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As usual, '''this part of the homework will contribute to the 10% of the grade'''; the deadline to submit the solution is '''before the you take the exam''' sending it to davide.eynard_at_gmail.com.
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* [http://davide.eynard.it/2012/06/05/octave-clustering-demo-part-0-introduction-and-setup/ Homework 2012 part 3:] follow this tutorial and answer the questions from all 5 sub-tutorials.
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== 2012 Homework ==
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The Homework of 2012 organized like an octave/matlab series of tutorials. You are requested to go through the tutorials and practice with the algorithms that have been presented in class. To prove us you have done it and that you have understood the code you will be requested to solve few exercises and provide us a pdf report by email
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* [[Media:PAMI_homework_2012_1.pdf | Homework 2012 part 1]]: this is the text of the first part of the homework; it has been intentionally edited not to allow cut and paste. '''This part of the homework will contribute to the 10% of the grade and the deadline to submit the solution by email to matteucci@elet.polimi.it and malago@elet.polimi.it is Tuesday 5/6 23:59'''
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** [[Media:prostate.data | prostate.data]]: the dataset used for the homework
+
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** [[Media:prostate.info | prostate.info]]: the dataset used for the homework
+
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** [[Media:textread.m | textread.m]]: (optional) function which might be useful depending on your octave version
+
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** [[Media:strread.m | strread.m]]: (optional) function which might be useful depending on your octave version
+
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'''Note:''' for some strange reason the CSM of the website has decided to rename the files with capitals, please save them in lower case :-(
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* [[Media:PAMI_homework_2012_2.pdf | Homework 2012 part 2]]: this is the text of the second part of the homework; it has been intentionally edited not to allow cut and paste. '''This part of the homework will contribute to the 10% of the grade; the deadline to submit the solution by email to matteucci@elet.polimi.it is the day before the exam you decide to attend''' (e.g., if you decide to take the exam on the 26/6 then you need to turn it in by 25/6).
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** [[Media:SAheart.data | SAheart.data]]: the dataset used for the homework
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** [[Media:SAheart.info | SAheart.info]]: the dataset used for the homework
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'''Errata Corrige''': there were a few bugs a bug in the homework text. I have updated the pdf and they were:
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In the code for loading the data I forgot to remove the first column which you do not need
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data = dlmread('SAheart.data',',',1,1);
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X = data(:,1:9);
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Y = data(:,10);
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In the StratifiedSampling function the sorted verctors should be assigned
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% just an ahestetic sorting
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testing = sort(testing);
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training = sort(training);
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In the computation of feature projection, the code for the maximization of a'B*a via SVD should be changed as it follows
+
−
% maximization of a'*B*a / a'*w*a via SVD
+
−
[Vw, Dw, Vw] = svd(W);
+
−
Whalf = Vw * sqrt(Dw) * Vw'; % Whalf'*Whalf == W
+
−
Wminushalf = inv(Whalf);
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Mstar = M*Wminushalf;
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    % Add this variable for computing Mstar mean
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    meanMstar = mean(Mstar);
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for i=1:size(M,1)
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    % Remove the mean saved before the loop
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    Mstar(i,:) = Mstar(i,:)-meanMstar;
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end
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Bstar = Mstar'*Mstar;
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[Vstar, Db, Vstar] = svd(Bstar);
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In the expansion to quadratic space the starting index for the inner loop should i and not 1. Moreover in some cases it might be possible to have columns which are duplicated (e.g., with boolean attribute); in this case you should not need the robust version of linear regression.
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function extendedX = expandToQuadraticSpace(X)
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    % adds new columns to extendedX; keeps X for other calculations
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    extendedX = X;
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    for i=1:size(X, 2)
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        for j=i:size(X, 2)
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            newColumn = X(:, i) .* X(:, j);
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            extendedX = [extendedX newColumn];
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        end
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    end
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    % remove duplicated columns
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    duplicates = [];
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    for i=1:size(extendedX, 2)
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        for j=i+1:size(extendedX, 2)
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            if(sum(extendedX(:,i)==extendedX(:,j)) == size(X,1))
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                duplicates = [duplicates j];
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            end
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        end
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    end
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    extendedX(:,duplicates) = [];
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end
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* [http://davide.eynard.it/2012/06/05/octave-clustering-demo-part-0-introduction-and-setup/ Homework 2012 part 3]: the third part of the homework is '''optional''', so you are not required to complete it. However, if you want to give it a try and use it to understand the topics covered by Davide Eynard in his lectures you are welcome. As usual, the questions in this homework are very close to the ones you will find in classworks, so we suggest to have a look at hose anyway! '''In case you decide to turn it in and have it contribute with a 10% to the grade, the deadline to submit the solution by email to matteucci@elet.polimi.it and davide.eynard@polimi.it is the day before you decide to take the exam''' (e.g., if you decide to take the exam on the 10/7 then you need to turn it in by 9/7)
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'''Note:''' homeworks are meant to let you see (and practice) a little bit with the topics presented during the course. They are evaluated because you spent some time on those and thus you deserve some credit for that ;-)
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== 2011 Homework ==
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Here you can find the homework for the year 2011 and the material you need to complete it. Please read the F.A.Q. below and for any unsolved doubt contact the teachers of the course.
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* [[Media:PAMI_homework_2011_v02.pdf | Homework 2011 v02]] a minor change in the signature of the logistic regression function
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* [[Media:PAMI_homework_2011_v01.pdf | Homework 2011 v01]] text with questions and exercises
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* [[Media:dataset.txt | Dataset]] for the clustering exercise in csv format
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'''Frequently Asked Questions'''
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* '''''How do I take the square root of a matrix?''''': check the diagonalization approach from [http://en.wikipedia.org/wiki/Square_root_of_a_matrix].
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* '''''How do I compute the chi square statistics?'''': in the slide there is a cut and paste error since e_ij=R_it*C_tj as described here [http://en.wikipedia.org/wiki/Pearson's_chi-square_test]
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* '''''When it is due? In which format?''''': The homework is due on the 29/06 and should be delivered by email. Send us (all the course teachers) the .m files in a zip archive attached to this email and a link to the pdf with the written part (not to flood our mailboxes).
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* '''''Can we do that in groups? How many people per group?''''': Yes, you can work on the homework in groups, but no more than 3 people per group are allowed. Put the names of all homework authors in the pdf and in all the .m files. If you discuss something with other people, w.r.t. the people in your group, point it out in the pdf file as well.
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* '''''Can we ask questions about the exercises or the code?''''': Yes you should! First of all, there might be unclear things in the exercise descriptions and those should be clarified as soon as possible for all (this is why the homework is versioned). But you could ask for help as well, our goal is to have you all solving all the questions and get a high grade ... but we will not do the homework on you behalf ;-)
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* '''''How the optional questions are graded?''''': They compensate for possible errors in the other questions; we suggest to work on them anyway to be sure you get the maximum grading.
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* '''''How the homework will be graded?''''': we are interested in understanding if you understood or not; thus we are not interested in the result, but we want to check how you get to the result. So please: 1) clarify all the assumptions and all the steps in your exercises 2) comment as much as possible your .m files!
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Revision as of 19:37, 6 March 2016


The following are last minute news you should be aware of ;-) 

09/03/2016: Lectures start today!!

Course Aim & Organization

This course will introduce basic concepts and techniques used within the field of autonomous mobile robotics. We analyze the fundamental challenges for autonomous intelligent systems when these move on wheels or legs and present the state of the art solutions currently employed in mobile robots and autonomous vehicles.

Teachers

The course is composed by a blending of lectures and exercises by the course teacher and a teaching assistant.

Course Program

Lectures will provide theoretical background and real world examples. Lectures will be complemented with practical exercises in simulation for all the proposed topics and the students will be guided in developing the algorithms to control an autonomous robot.

Among other topics, we will discuss:

  • Mobile robots kinematics,
  • Sensors and perception,
  • Robot localization and map building,
  • Simultaneour Localization and Mapping (SLAM),
  • Path planning and collision avoidance,
  • Exploration of unknown terrain.

Detailed course schedule

A detailed schedule of the course can be found here; topics are just indicative while days and teachers are correct up to some last minute change (I will notify you by email). Please note that not all days we have lectures!! 

Note: Lecture timetable interpretation
* On Wednesdays, in E.G.2, starts at 13:30, ends at 15:15
* On Thursday, in D.1.1, starts at 13:30, ends at 15:15
Date Day Time Room Teacher Topic
09/03/2016 Wednesday 13:15 - 15:15 EG3 Matteo Matteucci Course Introduction
10/03/2016 Thursday 13:15 - 15:15 D11 Matteo Matteucci Sensors and Actuators (motors)
16/03/2016 Wednesday 13:15 - 15:15 EG3 Matteo Matteucci Robot kinematics
17/03/2016 Thursday 13:15 - 15:15 D11 Gianluca Bardaro Gazebosim and URDF
23/03/2016 Wednesday 13:15 - 15:15 EG3 Gianluca Bardaro Differential drive in Gazebo
24/03/2016 Thursday 13:15 - 15:15 -- -- No Classes
30/03/2016 Wednesday 13:15 - 15:15 EG3 Matteo Matteucci Robot kinematics (cont.)
31/03/2016 Thursday 13:15 - 15:15 D11 Matteo Matteucci Robot navigation algorithms
06/04/2016 Wednesday 13:15 - 15:15 EG3 Gianluca Bardaro Middleware for robotics and ROS
07/04/2016 Thursday 13:15 - 15:15 D11 Gianluca Bardaro Control of diff drive in ROS
13/04/2016 Wednesday 13:15 - 15:15 EG3 Matteo Matteucci Trajectory planning (graph based)
14/04/2016 Thursday 13:15 - 15:15 D11 Matteo Matteucci Trajectory planning (graph Based)
20/04/2016 Wednesday 13:15 - 15:15 EG3 Gianluca Bardaro ROS Tools: tf, rviz, map server
21/04/2016 Thursday 13:15 - 15:15 D11 Gianluca Bardaro Trajectory planning and navigation in ROS
27/04/2016 Wednesday 13:15 - 15:15 EG3 Matteo Matteucci Trajectory planning (sample based)
28/04/2016 Thursday 13:15 - 15:15 D11 Matteo Matteucci Trajectory planning (sample based)
04/05/2016 Wednesday 13:15 - 15:15 EG3 Matteo Matteucci Simultaneous Localization and Mapping (SLAM)
05/05/2016 Thursday 13:15 - 15:15 D11 Matteo Matteucci SLAM with Lasers and EKF-SLAM
11/05/2016 Wednesday 13:15 - 15:15 EG3 Matteo Matteucci SLAM with Lasers and EKF-SLAM
12/05/2016 Thursday 13:15 - 15:15 D11 Matteo Matteucci Particle filters and Monte Carlo Localization
18/05/2016 Wednesday 13:15 - 15:15 EG3 Gianluca Bardaro ROS movebase (plus actionlib)
19/05/2016 Thursday 13:15 - 15:15 D11 Gianluca Bardaro ROS movebase continued
25/05/2016 Wednesday 13:15 - 15:15 EG3 Matteo Matteucci Planning complex tasks
26/05/2016 Thursday 13:15 - 15:15 D11 Matteo Matteucci Planning in the Blockworld
01/06/2016 Wednesday 13:15 - 15:15 EG3 Gianluca Bardaro Planning Domain Definition Language (PDDL)
02/06/2016 Thursday 13:15 - 15:15 -- -- No Classes
08/06/2016 Wednesday 13:15 - 15:15 -- -- No Classes
09/06/2016 Thursday 13:15 - 15:15 -- -- No Classes
15/06/2016 Wednesday 13:15 - 15:15 EG3 Matteo Matteucci Questions and Answers

Course Evaluation

Course evaluation is composed by two parts:

  • A written examination covering the whole program graded up to 27/32
  • A home project in simulation practicing the topics of the course graded up to 5/32

the final score will sum the grade of the written exam and the grade of the home project.

Home Project

In the home project you will use ROS and Gazebo to develop a simple autonomous mobile robot performin a simple task. The project will be presented mid May and you will have until the end of June to complete it.

Teaching Material (the textbook)

Lectures will be based on material from different sources, teachers will provide their slides to students as soon they are available taken from the book.


If you are interested in a more deep treatment of the topics presented by the teachers you can refer to the following books and papers:

Past Exams and Sample Questions

In the 2015/2016 Academic Year the course has changed significantly and the exam format as well. For this edition of the course we do not have past exams to share with you, should expect 2 theoretical questions + 2 practical exercises (on average), no coding exercise in the exams since you will see some coding already in the home project. During the lectures the teachers will suggest possible exercises you might encounter in the exams.

Online Resources

The following are links to online sources which might be useful to complement the material above

  • ROS: the Robot Operating System
  • Gazebo: the Gazebo robot simulator
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