Maximum information, minimum post

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:
http://www.aaai.org/ocs/index.php/WS/AAAIW11/paper/view/3960

Here's a fun picture of the robot!

Particle filters in real time

I love class projects, because it's great to make something that works amidst the theory and pure math. For one of my robotics classes, my team decided to code up a particle filter.

Actually, our plan was to have a Lego Mindstorms NXT robot localize itself (figure out its initially unknown position in a known environment), then navigate to a sound source while avoiding obstacles. We couldn't get the physical robot to cooperate, so we did the localization piece in simulation.

We got the particle filter working, and made some nice videos of the particles in action. Those and more info after the jump.