AI-powered robot mimics ANY action after watching a person do it just once, paving the way for machines to learn new skills in the same way as humans
AI-powered
robot mimics ANY action after watching a person do it just once, paving the way
for machines to learn new skills in the same way as humans
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Scientists
have developed a clawed machine that can learn several tasks
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It masters
new skills by watching a video of a human performing the action once
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This allows
the android to master new skills faster than other robots
·
It could
lead to machines that learn complex tasks purely through observation
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A new breed of AI-powered robots could soon
mimic almost any action after watching a human do them just once.
Scientists have developed a clawed machine that can learn
new tasks, such as dropping a ball into a bowl or picking up a cup, simply by
viewing a person perform them first.
Researchers said the trick allows the android to master
new skills much faster than other robots, and could one day lead to machines
capable of learning complex tasks purely through observation – much like humans
and animals do.
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A new breed of AI-powered robots could soon
mimic almost any action after watching a human do them just once. This
animation shows a person completing a simple task (left) that is then copied by
a new robot developed by scientists in California (right)
Project lead
scientist scientist Tianhe Yu wrote in a blog post:
'Learning a new skill by observing another individual, the ability to imitate,
is a key part of intelligence in human and animals.
'Such a capability would make it dramatically easier for
us to communicate new goals to robots – we could simply show robots what we
want them to do.'
Developed by engineers at the University of California at
Berkeley, the robot quickly learns new actions by watching a person do it on
video.
Clips of the android show it picking up fruit and putting
it into a bowl, as well as carefully moving around an obstacle following the
same path demonstrated by a scientist.
Most machines, such as the robots in car factories, are
programmed to complete tasks via computer code – a rigid and often
time-consuming process.
More recently, androids have been developed that can
learn by watching another robot complete the action, though they typically need
to mimic the task thousands of times before perfecting it.
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Scientists have developed a clawed machine
that can learn several tasks - such as dropping a ball into a bowl or moving a
plastic plant pot - by first viewing a person perform it
In the new paper, the UC team outline the technique which
allowed them to teach a robot actions with just one demonstration – vastly
speeding up the learning process.
They combined two different learning algorithms into a
single super-AI.
One of these – a meta-learning algorithm – helps a robot
to learn by incorporating the movements used in prior tasks rather than master
each skill from scratch.
The other, an imitation algorithm, allows the machine to
pick up a new skill by watching something else perform it.
Combining the two allowed scientists to build an AI that
draws on both prior experience as well as mimicry to build new skills in a
process the researchers call model-agnostic meta-learning (Maml).
This means it can learn to manipulate an object it has
never seen before by watching a single video – a breakthrough that could
accelerate machine learning.
Researchers wrote: 'Our method enables a PR2 robot to
effectively learn to push many different objects that are unseen during
meta-training toward target positions.'
The robot can also 'pick up many objects and place them
onto target containers by watching a human manipulates each object', they said.
HOW
DOES ARTIFICIAL INTELLIGENCE LEARN?
AI systems rely on artificial neural
networks (ANNs), which try to simulate the way the brain works in order to
learn.
ANNs can be trained to recognise patterns
in information - including speech, text data, or visual images - and are the
basis for a large number of the developments in AI over recent years.
Conventional AI uses input to 'teach' an
algorithm about a particular subject by feeding it massive amounts of
information.
Practical applications include Google's
language translation services, Facebook's facial recognition software and
Snapchat's image altering live filters.
The process of inputting this data can be
extremely time consuming, and is limited to one type of knowledge.
A new breed of ANNs called Adversarial
Neural Networks pits the wits of two AI bots against each other, which allows
them to learn from each other.
This approach is designed to speed up the
process of learning, as well as refining the output created by AI
systems.
n
future, the team said they plan to expand the range of tasks that the robot can
learn from humans.
The eventual goal is to develop machines that can
'quickly develop strategies for new situations', they said.
The study
was posted to the pre-publish journal Arxiv and has not yet undergone
peer review.
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