Robots that learn from experience and can solve novel problems – just like humans – sound like science fiction.
But a Japanese researcher is working on making them science fact, with machines that can teach themselves to perform tasks they have not been programmed to do, using objects they have never seen before.
In a world first, Osamu Hasegawa, associate professor at the Tokyo Institute of Technology, has developed a system that allows robots to look around their environment and do research on the internet, enabling them to “think” how best to solve a problem.
“Most existing robots are good at processing and performing the tasks they are pre-programmed to do, but they know little about the ‘real world’ where we humans live,” he said.
“So our project is an attempt to build a bridge between robots and that real world,” he said.
The Self-Organising Incremental Neural Network (SOINN) is an algorithm that allows robots to use their knowledge – or what they already know – to infer how to complete tasks they have been told to do.
In a laboratory demonstration, the machine begins to break down the task into a series of skills that it has been taught: holding a cup, holding a bottle, pouring water from a bottle, placing a cup down.
Without special programmes for water-serving, the robot works out the order of the actions required to complete the task.
In a separate experiment, SOINN is used to power machines to search the internet for information on what something looks like, or what a particular word might mean.
Mr Hasegawa’s team is trying to merge these abilities and create a machine that can work out how to perform a given task through online research.
“In the future, we believe it will be able to ask a computer in England how to brew a cup of tea and perform the task in Japan,” he said.
Like humans, the system can also filter out “noise” or insignificant information that might confuse other robots.
The process is similar to how people can carry on a conversation with a travelling companion on a train and ignore those around them, or can identify an object under different lighting and from various angles, Hasegawa said.
“Human brains do this so well automatically and smoothly so we don’t realise that we are even doing this,” he said.
“This robot can connect its brain directly to the internet,” he said.
Mr Hasegawa hopes SOINN might one day be put to practical use, for example controlling traffic lights to ease traffic jams by organically analysing data from public monitors and accident reports.
He also points to possible uses in earthquake detection systems where a SOINN-equipped machine might be able to aggregate data from numerous sensors located across Japan and identify movements that might prove significant.
In a domestic setting, a robot that could learn could prove invaluable to a busy household.
But, cautions the professor, there are reasons to be careful about robots that can learn.
What kinds of tasks should we allow computers to perform? And is it possible that they might turn against us, like in the apocalyptic vision of Stanley Kubrick’s film 2001: A Space Odyssey.
“A kitchen knife is a useful thing. But it can also become a weapon,” he said.