So, Where’s My Robot?

Thoughts on Social Machine Learning

Should your robot learn like a child?

Alison Gopnik recently had an opinion article in the NYTimes.  Gopnik is a Psychologist that studies child development and “Theory of Mind.”

I find much of Gopnik’s work inspiring for robot learning, and the ideas in this article are a good example.  She lays out evidence and findings related to the difference between adult and child learning.  In many ways children are much better at learning and exploring than adults.  They observe and create theories that are consistent with a keen probabilistic analysis of seen events.  These theories guide their “play” or exploration in a way that efficiently gathers information about their complex and dynamic world.

The description of adult versus child-like learning sounds like the traditional explore/exploit tradeoff in machine learning.  But this raises a question we are often asked with respect to robot learning, do we actually want robots to explore like children?  I think the answer is yes and no.  We probably don’t want robots to need a babysitter, but we do want robots to exhibit the kind of creativity and experimentation that you see in some of Gopnik’s studies of causal structure for example.

I’m most excited about the idea that Gopnik ends the article with: “But what children observe most closely, explore most obsessively and imagine most vividly are the people around them. There are no perfect toys; there is no magic formula. Parents and other caregivers teach young children by paying attention and interacting with them naturally and, most of all, by just allowing them to play.”

I think that the importance of social learning in human development is a strong argument for robot learning by demonstration or instruction—that we should be looking for the short cuts and computational gains we can get from leveraging a partner.

September 22nd, 2009 Posted by | Machine Learning, Situated Learning | no comments