Course Probabilistic Robotics
Master Artificial Intelligence
This is the information of Fall 2017
This course was previously given in the Bachelor Articial Intelligence (Fall 2011
The latest information (current year) can be found here
The description is available in the course catalogue of the UvA
The course is a constrained choice in the Master Artificial Intelligence Curriculum.
More details about the organization of the course can be found in the Course Manual.
Probabilistic robotics is a subfield of robotics concerned with the perception and control part.
It relies on statistical techniques for representing information and making decisions. By doing so, it accommodates the uncertainty that arises in most contemporary robotics applications.
This course is based on the book 'Probabilistic Robotics', from Sebastian Thrun, Wolfram Burgard and Dieter Fox. The book concentrates on the algorithms, and only offers a limited number of exercises. Their suggestion is to accompany the book with a number of practical, hands-on assignments for each chapter. The assignments of this course are designed to understand the basic problems concerning mobile robotics.
The course will take place in a studio classroom setting. A list of Frequent Asked Questions will be maintained.
Students, who were not able to attend a lecture, can catch up by listing to the recordings of Burghard's lectures (English).
The assignments are based on the Octave or Matlab environment.
This YouTube lectures give a short introduction to some essentials of the Matlab environment:
the workspace, variables, vectors, colon operator, matrices, concatenating, matrix initialization. Also note Octave cheat sheet.
Week 36: Chapter 1 - Introduction & Robot Paradigms
Recording Robot Paradigms
Exercise 2.8.1, 2.8.2 & 2.8.3 and 2.8.4.
Week 36: Chapter 2 State Estimation & Chapter 3.2 - Gaussian Filters - Kalman Filter
Recordings State Estimation part 1 and part 2 & discussed Burghard's lecture
Discussed solutions of assignments
2.8.1, 2.8.2 & 2.8.3 and 2.8.4
Week 37: Chapter 3.3 - 3.5 Extended Kalman Filters & Geometric Approach
Recording Extended Kalman Filter
Assignment 3.8.1 and 3.8.2.
Week 37: Chapter 4 - Nonparametric Filters: Discrete and Particle Filters
Recordings Discrete Filters and Particle Filters (part 1 & part 2)
Assignment 4.6.1 and 4.6.4.
Week 38: Chapter 5 - Wheeled Locomotion & Robot Motion Models
Recordings Wheeled Locomotion (part 1, part 2, part 3, part 4) and Motion Models (part 1, part 2, part 3).
Motion model Assignment.
Week 38: Chapter 6 - Sensors & Robot Perception Models
Recordings Sensors (part 1) and Sensor Models (part 1, part 2).
Stanley's RaceDay movie and Stanley's Tech lecture recording (Dutch)
Week 39: Chapter 9 - Mapping with known poses
Recordings (part 1, part 2, part 3, part 4).
EKF - SLAM Assignment, including first, second and third dataset.
Chapter 10 - SLAM
montemerlo-fastslam-aaai video, corresponding to AAAI'03 paper
Recordings (part 1, part 2).
animation of raw data of Intel Research Lab, animation of CMU's Wean Hall
Week 40: Monday no lecture - conflict with Delft Workshop on Robot Learning
Week 40: Chapter 13 - The FastSLAM Algorithm (landmark based and grid based)
Recordings (part 1, part 2).
Chapter 11 - GraphSLAM and Chapter 12 - SEIF SLAM.
video GroundHog, video mine map, video GraphSLAM with Segbot
FastSLAM with Known Data Association Assignment.
Chapter 17 - Exploration and 3D Mapping
Recordings (exploration and 3D mapping).
video CMU 1999 multi-robot exploration, video exploration with limited communication
Week 42: Final - Sebastian Thrun's TED talk.
Week 43: Exam ('open book') of Chapter 1-13 & 17 of the book, Thursday October 26, 9:00-12:00, A1.02.
Week 2: Reexam ('open book'), Thursday January 11, 9:00-12:00, room A1.06
Sebastian Thrun, Wolfram Burgard and Dieter Fox, Probabilistic Robotics, The MIT Press, 2005.
- Week 36: until section 3.3 - page 54
- Week 37: until chapter 4 - page 116
- Week 38: until chapter 6 - page 187
- Week 39: until chapter 10 - page 335
- Week 40: chapter 13 - page 437-483
- Week 41: chapter 11,12,17 - page 336-436 and 569-605
Embedding in AI curriculum
This course is supported by the following chapters of 'Artificial Intelligence - A Modern Approach'
3rd edition, by Stuart Russell and Peter Norvig:
- Chapter 13: Quantifying Uncertainty
- Chapter 14: Probabilistic Reasoning
- Chapter 15: Probabilistic Reasoning over Time
- Chapter 25: Robotics
The course was evaluated by the participants with a 6.0:
Albert-Ludwigs-University Freiburg: Introduction to Mobile Robotics (2017) by Wolfram Burgard, Marina Kollmitz, Oier Mees and Daniel Büscher.
Albert-Ludwigs-University Freiburg: Robot Mapping (2016-2017) by Wolfram Burgard.
University of Washington CSE 571: Probabilistic Robotics (2016) by Dieter Fox and Arunkumar Byravan
Albert-Ludwigs-University Freiburg: Introduction to Mobile Robotics (2016) by Wolfram Burgard, Michael Ruhnke and Bastian Steder.
University of Washington CSE 571: Probabilistic Robotics (2015) by Dieter Fox and Arunkumar Byravan
George Mason University: Autonomous Robotics (2015) by Jana Kosecka.
Albert-Ludwigs-University Freiburg: Introduction to Mobile Robotics (2015) by Wolfram Burgard and Gian Diego Tipaldi.
Albert-Ludwigs-University Freiburg: Introduction to Mobile Robotics (2014) by Wolfram Burgard, Maren Bennewitz, Gian Diego Tipaldi, Luciano Spinello, Mladen Mazuran and Tim Welschehold.
Udacity by Georgia Tech: Artificial Intelligence for Robotics (2012) by Sebastian Thrun.
Albert-Ludwigs-University Freiburg: Introduction to Mobile Robotics (2011) by Wolfram Burgard, Cyrill Stachniss, Maren Bennewitz, Kai O. Arras, Juergen Hess, Joerg Mueller.
City College of New York G3300: Advanced Mobile Robotics (2011) by John (Jizhong) Xiao
Stanford University CS 226: Statistical Techniques in Robotics (2010) by Sebastian Thrun and Alex Teichman.
Albert-Ludwigs-University Freiburg: Introduction to Mobile Robotics (2009) by Wolfram Burgard, Cyrill Stachniss, Maren Bennewitz, Kai O. Arras, Giorgio Grisetti, Daniel Meyer-Delius, Boris Lau
City College of New York G3300: Advanced Mobile Robotics (2009) by John (Jizhong) Xiao
University of Southern California CSCI 445: Introduction to Robotics (2008) by Maja Mataric, Laurent Itti and Randolph Voorhies.
University of Washington CSE 571: Probabilistic Robotics (2007) by Dieter Fox, Jonathan Ko, Brian Ferris
Technische Universitaet Dresden: Probabilistic Robotics (2007) by Axel Grossmann.
Albert-Ludwigs-University Freiburg: Introduction to Mobile Robotics (2007) by Wolfram Burgard, Kai O. Arras, Cyrill Stachniss, Giorgio Grisetti, Jürgen Sturm, Boris Lau
Stanford University CS 226: Statistical Techniques in Robotics (2006) by Sebastian Thrun and Jason Chuang.
University of Washington CSE 481: Robotics Capstone (2006) by Dieter Fox, Dirk Haehnel, Fred Potter
Stanford University CS 226: Statistical Techniques in Robotics (2004) by Sebastian Thrun and Rahul Biswas.
Last updated November 15, 2017
This web-page and the list of participants to this course is maintained by
University of Amsterdam