I was at a conference about humanoid robotics, and particularly the iCub, yesterday and Mark Lee from the University of Aberystwyth was talking about the difficulties of ‘raising’ truly developmental robots: robots that learn about their bodies and environments through experience the way we do. This got me thinking.
Being an analog girl at heart, and given that robots have a lot of essentially analog components (even if they are driven with digital controllers), I’d always assumed that truly intelligent humanoids would haveto be raised developmentally. Each individual would have to learn about it’s unique set of motors and sensors and processors and what they could do and how they could interact with the world before they would be able go out and do things. Now I wonder if it has to be as drastic as that.
My thinking now is that the question turns on just how different each robot will be to its ‘siblings’: robots turned out in the same batch and that are (at least intended to be) identical. There are bound to be subtle differences because of manufacturing tolerances, but perhaps these don’t matter so much for digitally-driven machines. After all, a robot has to be able to cope if some of its components fail or change their performance (for instance) due to changes in temperature. So perhaps we could clone the ‘brains’ of one robot and successfully transplant it into another, which would just wake up feeling a bit out of sorts and have to re-optimize.
If that’s true, it brings up a lot of interesting questions. Is the best idea is to focus energy on the development of a single machine to clone, or to raise a whole bunch of robots to a certain point and then, in a natural-selection-type way, clone the best brains and ditch the rest? Perhaps the latter would be the best way to learn the best teaching methods for robots? And at what point in their development should they be cloned? Presumably you don’t want to have to send a ‘baby’ robot to each new workplace so that it can learn about it’s new environment and tasks at the same time as it’s learning how to see and control its actuators from scratch. On the other hand you want it to have plenty of scope for adapting to its new environment…
Photo: Yan Wu working with the Imperial College London iCub.
Originally posted on Brains and Machines.