1. CMPUT 412 Experimental Mobile Robotics Csaba Szepesvári University of Alberta

2. Plan for today   Topic of the course   Introduction/admin   Expectations   Requirements/Marking   Course contents

3. Topic: Autonomous Driving  Goal: Cars should drive themselves  Advantages: Less accidents, increased efficiency  History: 1961: Stanford cart 1987-1995: Dickmanns 180km/h, 1000km, human intervention, driving on highways 2005: DARPA Grand Challenge, 211 km desert course

4. 2007 2007: DARPA Urban Challenge Autonomous parking systems (Lexus, Mercedes, Toyota,..) Cybercars project

5. Course project

6. The tasks  Following prespecified routes in a "city"  Following prespecified routes in a "city" [14 January - 28 January] [14 January - 28 January] Task #1: Build a robot that follows a white tape, taped to the floor Task #1: Build a robot that follows a white tape, taped to the floor Task #2: City-like environment, follow a prespecified route Task #2: City-like environment, follow a prespecified route  Taxiing on demand, obstructions on the road [29 January - 25 February] [29 January - 25 February] Task #1: Picking up passengers at various locations and taxiing them to other locations. Uses bluetooth Task #1: Picking up passengers at various locations and taxiing them to other locations. Uses bluetooth Task #2: Parts of the route can become blocked. Task #2: Parts of the route can become blocked.  Parking, dealing with traffic [26 February - 7 April] [26 February - 7 April] Task #1: Parallel parking Task #1: Parallel parking Task #2: Multiple robots on the road at the same time, avoiding collisions Task #2: Multiple robots on the road at the same time, avoiding collisions

7. Admin

8. Me..  Studies: Mathematics (Stat. and Prob.) Mathematics (Stat. and Prob.) Computer Science Computer Science  Research Reinforcement learning (theory) Reinforcement learning (theory) Machine learning (vision, robotics,..) Machine learning (vision, robotics,..)  Experience 5 years in industry (sw firm, speech, text, video ) 5 years in industry (sw firm, speech, text, video ) 15 years of C++ 15 years of C++

9. You..?  Fill out form!

10. Schedule!  Lecture: TR 15:30-17:00  Room: ETL E1 018  Lab: W 14:00-17:00  Room: CSC 229

11. Office hours  Appointment: szepesva@cs.ualberta.ca szepesva@cs.ualberta.ca szepesva@cs.ualberta.ca  Stop by!  Room: Ath 3-11  Phone: x2-8581

12. Information sources  Course webpage: ugweb.cs.ualberta.ca/~c412 ugweb.cs.ualberta.ca/~c412  RLAI page: http://rlai.cs.ualberta.ca/openpages2/CMP UT412+2008 http://rlai.cs.ualberta.ca/openpages2/CMP UT412+2008 http://rlai.cs.ualberta.ca/openpages2/CMP UT412+2008  Teaching Assistants: Azad Shademan, Neesha Desai  Instructional group support: John Rodson (rod@ugrad.cs.ualberta.ca) John Rodson (rod@ugrad.cs.ualberta.ca)rod@ugrad.cs.ualberta.ca

13. Prerequisites  No official prerequisites  Brush up your knowledge in: Calculus (MATH 114,115, 214) Calculus (MATH 114,115, 214) Linear algebra (MATH 120 or 125) Linear algebra (MATH 120 or 125) Probability & stat (STAT 221) Probability & stat (STAT 221)  Good to know about Machine learning (CMPUT 466/551) Machine learning (CMPUT 466/551)  Programming: C/C++ C/C++

14. Expectations

15. Goals  Learn about robotics Concepts Concepts Techniques Techniques Challenges Challenges  Complete the project  Have fun!

16. Course format  Formal lectures (~3 weeks) Background Background  Weekly project meetings Discussion of progress Discussion of progress Challenges Challenges Planning (to meat deadline) Planning (to meat deadline)  Steady workload

17. My contribution  I act like a supervisor I define tasks I define tasks I provide background material (lectures, resources) I provide background material (lectures, resources) I answer questions I answer questions I evaluate your performance I evaluate your performance

18. My expectations  You try your best to solve the assignments (it’s fun!)  You will act in a self-initiated manner  You ask questions  Cooperate, but contribute  You come to the lectures  You come to the labs  No cheating, plagiarism, misrepresentation of facts (see course webpage for detailed info)

19. Marking

20. How to get (good) marks?  This is a project based course => no final, or midterm  “Just” solve the problems

21. Marking  No predefined grading system  Individual performances: Based on the reports, participation in the meetings, presentations, lectures  You are going to evaluate each others’ presentations

22. (Your) class presentations  Be prepared  Keep structure: Problem definition Problem definition Proposed solution Proposed solution Evaluation Evaluation Conclusions Conclusions  Use slides  Keep time limits [20 min]

23. Reports  Title, authors  Introduction (max. 1 page): the task and the challenges faced the task and the challenges faced  Proposed solution (max. 1 page + figs): possible alternatives, the decisions you took, how you arrived at them, measurements, data, related work possible alternatives, the decisions you took, how you arrived at them, measurements, data, related work  Evaluation (max. 1-2 pages + figs): Evaluate your solution. Show to what extent it achieves the goal, describe its limitations Evaluate your solution. Show to what extent it achieves the goal, describe its limitations  Conclusions (max. 1 page) summarize your work summarize your work  Sharing the work (1 page): Who did what, how was the time spent Who did what, how was the time spent  Format: Preferably use LaTeX, high quality, compressed presentation Preferably use LaTeX, high quality, compressed presentation

24. 0th assignment  Task #1: SUBSCRIBE to the course’s open pages!  Task #2: Learn to use the open pages  Open pages: Anyone can edit them (in the browser) Anyone can edit them (in the browser) Send notification to subscribers Send notification to subscribers Add comments/questions to the end of the page Add comments/questions to the end of the page