Having worked in the area of robotics while in graduate school, I was once fairly up-to-date on the latest ideas in the field. However, I never studied the probabilistic robotics ideas of Sebastian Thrun (which were first published during my graduate school years), although I was vaguely familiar with what he was doing. The MOOC CS 373, Artificial Intelligence for Robotics, provided me with an opportunity to rectify this gap in my knowledge. I completed the course over a period of several months.
As with most of Udacity's offerings, straight lecture segments run for a maximum of three to four minutes. In a conventional classroom, a student is part of a captive audience. With a MOOC, I found that even 30 seconds of boredom resulted in my opening of new tabs on my browser to seek something more interesting. So this particular maximum duration strikes me as a savvy move. Some segments are as short as a minute. Each segment culminates in some kind of interactive activity. In some cases, this involves thinking for a moment and answering a multiple-choice question. In other cases, the student must write or modify a computer program (in Python).
The course is divided into six units:
- Localization and review of probability theory
- Kalman Filters for tracking
- Particle Filters for localization
- Path Planning with A* and Dynamic Programming
- PID Control
Each unit consists of a series of interleaved lectures and exercises, ending with a "problem set". After the last unit is a "final exam".
As I watched the lectures, I actively took notes. It was often really helpful to re-wind the lectures multiple times to clarify an obscure or confusing point. Although it is possible in Udacity's environment to do all of the programming within the web browser, I always made a local copy of the code on my own machine. This was especially helpful for keeping the code around for future reference.
In general, the presentations were lucid and clear. I have already begun implementing particle filters on my Lego Mindstorms NXT robots. I am also looking forward to finding opportunities to implement a PID controller and to use GraphSlam. I found this course to be much easier to follow than Thrun's book Probabilistic Robotics. This course and that book work well together, actually, because the course provides great context for understanding what he is trying to do in the book.
I found the particle filter, PID controller, and GraphSlam lessons to be particularly effective in slowly building up the material in a comprehensible way. Especially in GraphSlam, the transition from 1D to 2D and from offline to online was quite effective.
One of the goals of the course is to employ Bayes Rule as a unifying concept among the various probabilistic techniques. The effectiveness of this strategy diminished as the course continued. By the time we got to GraphSlam, the relationship back to the Bayesian foundation was not at all clear.
Related to this is the explicit aversion to explaining derivations in the lectures. On one level, this is entirely comprehensible; for many students, such things are merely a distraction. On the other hand, much of the content felt superficial and arbitrary. A great option would be to have bonus lectures outside of the main sequence containing a deeper overview of the derivations.
The code itself was often put together rather casually. For example, the 2D GraphSlam exercise requires us to take noise into account. I neglected to do this in my solution, and it worked perfectly! The reason it worked is that the motion noise and measurement noise were equivalent. If they had been set to different values, students would not be able to get away with ignoring the noise as I had.
Another problem with the code is the degree to which good software engineering practices are not modeled well for the students. Global variables abound, and are often freely intermixed with parameters inside of function definitions.
The use of Python is not always idiomatic; many of my implementations were much shorter than those presented. List comprehensions, for example, can eliminate many multi-line loops.
The Twiddle exercises were particularly weak. The second one, in which the instructor confesses not actually having solved it, was especially annoying.
My biggest disappointment with the course, however, was the lack of coverage of essential material. In particular, how does one identify a landmark for use in GraphSlam from sensor data? The glib answers in the Q&A betrayed this gap. "Use RANSAC." "Create local 5x5 meter maps." "Invert omega to get the covariance matrix." These questions suggest that the course ended too quickly and abruptly. It is interesting how, on the one hand, a 14-week course is derided as an arbitrary length, but on the other hand, this course is cut off at six weeks while neglecting a critical part of the material necessary for programming a robot.
Another nice extension would be to incorporate FastSlam into the course. This would build nicely off the excellent coverage of particle filters in Unit 3. I would love to see some lectures inviting a comparison and contrast between the two SLAM methods.
I enjoyed this course, and given my goals it was a good use of my time, in spite of the flaws I described above.
It is really hard for me to call this a "course". It does not map well to what I do in a semester of teaching. I definitely cover a lot more material (both in lecture and in assignments). But it is a great collection of lectures.
In a follow-up post, I give my thoughts on how MOOCs relate more specifically to the experience at a liberal arts college.