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!

Neural networks part 1: Teaching Canyonero to drive

Artificial neural networks (ANNs) are modeled after natural neural networks (brains and nervous systems) and though they don't work exactly alike, both a brain and an ANN can learn arbitrarily complex tasks without being told exactly how - they just need data about the task and their performance.

A generic artificial neural network.

ANNs have been applied to a lot of artificial intelligence and machine learning problems, from autonomous vehicle driving to recognizing handwritten address on envelopes to creating artificial intelligence for video game agents.
  
I won't go deep into the math behind ANNs here; there are great sites on the web (and it's not really difficult, there's just a lot of bookkeeping). 

Instead, I'll take two posts to describe a couple of neural net projects I've worked on. First up: a mobile robot called Canyonero that learned to compensate for its own mismatched wheels.

Canyonero, with a camera in the front and a netbook running an ANN.