I've been planning for a while to write up some research I worked on in 2011 involving intrinsic "motivation" for robots. We got a workshop paper out of it, and I presented the results to the ECE department last year. I also planned to extend it into my thesis project.
But... the lab went through some advisor round-robin and the project fell apart, and I just don't feel like writing it up into a full post anymore.
In a nutshell, our robot learned a policy for a partially observable Markov decision process (POMDP) to learn about objects in a space by manipulating them with its arm, then assigning object classification probabilities, with Shannon information gain across all objects as the learning reward.
Here's the AAAI workshop abstract, with a link to the full PDF:
Here's a fun picture of the robot!