While we’re on the topic of biologically-inspired learning, here’s a video that recently hit YouTube of Alex Stoytchev’s robot. Their upper-torso robot is designed to learn by exploring its environment. In particular, they want to figure out how to get it to explore its environment and learn about objects in child-like ways. This is a problem that has been called affordance learning, learning what what effects objects in your environment produce when acted upon. In the video they demonstrate learning to classify 20 objects by sound only, with an action set of 5 exploratory actions.
I think affordance learning is an interesting topic for robots. We’re working on a slightly different problem than Alex’s lab, looking at how the robot can use people to help it learn about objects. But the end goal is the same, robots that could dynamically adapt to new environments without having to be pre-programmed with every skill needed.
Some work out of my lab on social robot learning was recently presented at ICDL in June (and got *best student paper!*) and follow-up work is going to be presented at RO-MAN in a couple of months. Here’s an overview + video…
Social learning in robotics has largely focused on imitation learning. Here we take a broader view and are interested in the multifaceted ways that a social partner can influence the learning process. We implement four biologically inspired social learning mechanisms on a robot: stimulus enhancement, emulation, mimicking, and imitation, and illustrate the computational benefits of each. In particular, we illustrate that some strategies are about directing the attention of the learner to objects and others are about actions. Taken together these strategies form a rich repertoire allowing social learners to use a social partner to greatly impact their learning process. We demonstrate these results in simulation and with physical robot “playmates”. (Find links to the papers here)
Previously, I’ve talked about approaching social robot behavior generation as an animation problem. I think this is a fun challenge because in many ways animating a social robot is similar to a Pixar character like Wall-E or Luxo Jr., but there are interesting ways in which is it different.
I’m thinking of this again since the Luxo Jr. animatronic recently made it’s debut at the Disney Hollywood Studios! It is quite fun to see this classic character come to life. And it is interesting to see the differences between the robot character and the on-screen version. I think in large part because it has much slower motion, this creates a completely different personality. However, it is still quite expressive and fun.
I recently had a couple of interesting encounters with Amazon.com recommendations that are a nice examples of where I think Socially Guided Machine Learning could come into play. Learning about how to get things done in dynamic human environments is a hard problem, and maybe the best way to solve it is to let people help.
I have always been really happy with Amazon’s recommendations, I think because until recently I only bought books there. And their similarity metric for books and other such media works pretty well. A couple of months ago I became a parent, and started buying several non-book purchases on Amazon. And for many such purchases the similarity metric breaks down.
One example was diapers, right after I bought newborn diapers Amazon recommended that I buy diapers for toddlers. The second example is pictured above. I bought wooden letters to spell my son’s name, one of which was the letter “A” so amazon recommends “N.” This one I found particularly amusing. Imagine buying these wood letters at a store, and you pick up the letter “A” and a person comes over, “Oh, if you like the letter ‘A’ you’ll really love ‘N’ take my word for it!”
These two examples point out a hard problem with statistical machine learning. Coming up with the right similarity metric is often an art, and in the case of the multitude of things that are available on Amazon it is hard to imagine a similarity metric that would work well across their whole site. Sometimes their metric works and sometimes it doesn’t. The reason I bought the letter “A” is not actually very similar to the reason that I might buy the letter “N.” And the person that buys size 1 diapers is not actually that similar (in the timescale of minutes) to the person that will be buy size 4 diapers. There is additional knowledge about the world that comes into play in making this decision. And I think the interesting challenge for Socially Guided Machine Learning is to develop ways that people can help machines develop the right similarity metrics for decision making in various contexts.
Update: Another amusing email from Amazon.com, re the wooden letters… They are trying hard to learn more!
IJCAI 2009 is coming up (July 13-16th in Pasadena). If you are going to be there don’t miss the Robot Exhibit. Chad Jenkins and Monica Anderson have put together a great event. There are several “challenge” topics, one of which is Learning by Demonstration.
I think it is great to see robots at IJCAI/AAAI, as it is a well recognized vehicle to push the field of AI forward into real world (what Horvitz has described as open world) problems. The robot exhibit is going to be a yearly event, held in conjunction with various conferences (AAAI, IJCAI, and others). The 2010 event will be held at AAAI, and the challenge topics will be announced during this year’s event.
