I've been working on robotics research inside the RoboCup domian since 2002. In particular in the Four-Legged league using the Sony Aibo robot. From 2008 we are also using the Aldebaran Nao humanoid robot.

RoboCup forces you to build a complete multi-agent robotic system. To produce a successful robotic soccer agent you need to have all the key subsystems working well. These systems are Vision, Localisation, Behaviour and Motion. In general robotics research a lot of research is focused on building these sub-systems, however many researches tend to underestimate the effort (and skill) required to make all these systems work well together.

Over the years I have written code in all these major subsystems, but I tend to enjoy working in localisation, behaviour and motion layers.

We are currently work on both an Autonomous Vehicle that competed at the 2007 DARPA Urban Challenge and a Autonomous Intersection Management system to control the flow of traffic at intersections.

The aim of DARPA bootstrap learning project is to create an electronic student that can learn a variety of tasks by taking lessons from a domain knowledgeable teacher. The student is attempting to mimic human learning and therefore must be capable of learning in wide variety of manners, some examples include `Learning by telling', 'Learning by example' and `Learning by feedback'. At UT we are focused on `Learning by feedback' and in particular applying reinforcement learning to a teacher-student setup.

Currently the DARPA page (http://bootstraplearning.com/) contains a very brief introduction but more information should be presented here at some stage. Other details can be found by looking at various press releases [1] [2] that were released in 2007.

Note: This bootstrap learning project should not be confused with Ben Kuipers and his students use of the term `bootstrap learning', this is an unfortunate naming conflict. However you should check out their work as its cool stuff !