Open ended modular robotic learning system are being developed in the UK. Evolutionary Modular Neural Nets uses their animat robots as a test bed. Basically it is way to develop some flexibility and learning in a small but manageable neural net when you are unable to evolve a larger neural net to handle all of the desired tasks.
They aim to grow a network which can control all aspects of the robot’s behaviour. This is accomplished by both growing the robot’s body and its mind (neural network) at the same time. The approach is inspired by the evolution of animals from simple to complex forms over evolutionary time periods. After all, if you want to develop a complex robot why not take the same route as biology did? Start with a simple machine and slowly build up its brain, body and environment together. In our system it is done by starting with a very simple robot and gradually adding small neural networks (modules) to its “brain” until it can function well. Then the robot (and the environment it interacts with) is allowed to become slightly more complex and the process of adding further network modules is repeated. This process continues until the robot can fulfil all its desired tasks. The previously added functionallity stays (it does not evolve further, only the newly added modules evolve) and the newer parts build up like the layers of an onion. This means that, on each iteration, the search space remains small. The idea is shown in the illustrations below.
Existing robots cannot usually cope with physical changes – the addition of a sensor or new type of limb, say – without a complete redesign of their control software, which can be time-consuming and expensive.
So artificial intelligence engineer Christopher MacLeod and his colleagues at the Robert Gordon University in Aberdeen, UK, created a robot that adapts to such changes by mimicking biological evolution. “If we want to make really complex humanoid robots with ever more sensors and more complex behaviours, it is critical that they are able to grow in complexity over time – just like biological creatures did,” he says.
As animals evolved, additions of small groups of neurons on top of existing neural structures are thought to have allowed their brain complexity to increase steadily, he says, keeping pace with the development of new limbs and senses. In the same way, Macleod’s robot’s brain assigns new clusters of “neurons” to adapt to new additions to its body.
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