Highlights of the AAAI Fall Symposium 2014: Knowledge, Skill, and Behavior Transfer in Autonomous Robots

Last week, I had the privilege of attending the 2014 AAAI Fall Symposium on Knowledge, Skill, and Behavior Transfer in Autonomous Robots.  My own interest in this topic comes from my goal of being able to program a mobile robot by driving it around.  I presented a paper describing an approach for doing this.  It improves upon my previous work by automatically associating actions with the Growing Neural Gas nodes, rather than relying on human input for specifying an action for every node.

To demonstrate the versatility of what I had implemented, on the morning of the talk I drove my Lego Mindstorms EV3 robot (running leJOS) around part of my hotel room for a couple of minutes, teaching it to avoid obstacles.  I then included in my presentation a nice video of the purely visual obstacle avoidance it had learned in this short time span.

Even more fun was the poster session.  I had promised to bring the robot with me to the poster session at the end of my oral presentation.  At the start of the poster session, I drove the robot around the poster area for about two or three minutes.  I was careful to make sure I introduced it to numerous human legs, so that it would learn to avoid them.  I then set it loose, and it demonstrated very nice visual obstacle avoidance for about the next hour or so.  When learning, it hadn't seen any white sneakers, so it did run into a couple of people, but for the most part it did great!

I'm hoping to release the source code for my implementation once the semester ends and I have a chance to clean things up a bit.

There were some interesting trends in the presentations I saw.  Several people, including Peter Stone and Stéphane Doncieux, presented work in which learning happened in simulation. Stone's work "closed the loop" by transferring the learned skills to a physical robot, and even learned from the physical robot to retrain the simulation. This was an aspect of transfer I hadn't really thought about very much.

Several other presenters, including Gabriel Barth-Maron, David Abel, Benjamin Rosman, and Manuela Veloso, were focused on Markov Decision Processes and reinforcement learning. That isn't really the focus of my current work, although I've explored it in the past and I may do so again in the future.  Much of the presented work involved ways of extracting transferrable domain knowledge from an MDP policy that had been learned with a particular reward function.  By transferring that knowledge to learning a policy for a new reward function but in the same domain, learning can be accelerated.

Finally, there were several presenters, including Tesca Fitzgerald, Andrea Thomaz, and Yiannis Demiris, who showcased work in demonstration learning.  The first two are interested in humans demonstrating tasks for humanoid robots, by either showing start/stop states for arrangements of objects, or even by directly manipulating their arms.  Of particular interest was Tesca Fitzgerald's efforts towards developing a spectrum of related tasks for which knowledge transfer is possible to varying degrees.  Task knowledge that is not transferred is handled with a planner; she had a great video of a robot hitting a ping-pong ball to illustrate what she had in mind.  Yiannis Demeris does a lot of work with assistive robotics for the disabled.  He showed some fascinating work in which the robots learned to help their clients, but only to the degree that the clients wanted the help.  He's also done some work with robots teaching humans various tasks.  Particularly amusing were the humanoid robot dance instructors!

I received some great feedback on my research from Yiannis Demiris, Nathan Ratliff, Matteo Leonetti, Eric Eaton, Pooyan Fazli, Matthew Taylor, Gabriel Barth-Maron, David Abel, Benjamin Rosman, Tesca Fitzgerald, Bruce Johnson, Cynthia Matuszek, and Laurel Riek.  I returned home with enough new ideas to keep me very busy for the next couple of years.  Thanks again to everyone who helped make this a great symposium!

Obtaining a tenure-track position in Computer Science at a liberal arts college

The essay Beyond Research-Teaching Divide has some good insights for applying for a tenure-track job at a liberal arts college.  First, a concise overview as to what this type of career entails, which is certainly consistent with my experience at Hendrix College:

The faculty members of many small colleges enjoy robust support with reasonable expectations for research output. We teach eager, inquisitive students who respect the title of “professor” (even when they do call you by your first name), whose whip-smart input enriches research almost as much as engaging with graduate students can.

The author then describes a valuable lesson learned:

I learned how to see small departments’ needs and gaps, thereby arming me to write directly to issues that did not necessarily announce themselves in job postings. Is a history department relying on its Latin Americanist to cover its Canadian history offerings? Mock up a syllabus that will lighten that load, and remark on it in your job letter. ... [W]hen small colleges hire, my experience shows that they hire people who have expertise their department lacks.

While this specific example is not directly pertinent to applying for a computer science position, the general concept definitely is.  Get to know the current faculty.  Determine their interests and aptitudes.  Look at what they publish and what they habitually teach.  From there, try to show how you would strengthen their program.  Typically, what a small department seeks is to increase its breadth.  In your cover letter, talk about how you could contribute in this way.

This last paragraph also rings true:

[S]trong letters are those that help us see a potential future colleague in front of a classroom, sharing a coffee with one of our students, and seated around our department’s meeting table (yup, we fit around one table; it’s probably not the room-filled affair you may have attended in graduate school). The best letters tell us more than what you think; they help us feel why you care about sharing those ideas with undergraduates in a classroom as much as with peer scholars in journals and books. Such letters exude enthusiasm for teaching without getting mired down in tedious assignment examples; they indicate your ability to model the research process, or (better) how you actively involve undergraduates in your research agenda.

Both of these last two excerpts are extremely helpful advice for writing a cover letter for a job at a small liberal arts college.  Study the department's web pages, as well as the college's catalog information for the program.  And be sure to convey your enthusiasm for teaching a variety of courses at all levels, especially outside your research area.  Mentioning one or two areas of genuine interest beyond your specialty can definitely be helpful.