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Keterampilan baru Robot master  melalui trial and error

Date:

May 22, 2015

Source:

University of California - Berkeley

Summary:

Ringkasan :

Para peneliti telah mengembangkan algoritma yang memungkinkan robot untuk belajar tugas motorik melalui trial and error menggunakan proses yang lebih dekat mendekati cara manusia belajar , menandai tonggak utama di bidang kecerdasan buatan ......read more

Robot masters new skills through trial
and error

Date:

May 22, 2015

Source:

University of California - Berkeley

Summary:

Researchers have developed algorithms that enable robots to learn motor
tasks through trial and error using a process that more closely approximates
the way humans learn, marking a major milestone in the field of artificial
intelligence.

..........................

Researchers at the University of California, Berkeley, have developed
algorithms that enable robots to learn motor tasks through trial and error
using a process that more closely approximates the way humans learn, marking a
major milestone in the field of artificial intelligence.

They demonstrated their technique, a type of reinforcement learning, by
having a robot complete various tasks -- putting a clothes hanger on a rack,
assembling a toy plane, screwing a cap on a water bottle, and more -- without
pre-programmed details about its surroundings.

"What we're reporting on here is a new approach to empowering a robot
to learn," said Professor Pieter Abbeel in UC Berkeley's Department of
Electrical Engineering and Computer Sciences. "The key is that when a
robot is faced with something new, we won't have to reprogram it. The exact
same software, which encodes how the robot can learn, was used to allow the
robot to learn all the different tasks we gave it."

The latest developments will be presented on May 28, in Seattle at the
International Conference on Robotics and Automation (ICRA). Abbeel is leading
the project with fellow UC Berkeley faculty member Trevor Darrell, the director
of the Berkeley Vision and Learning Center. Other members of the team are
postdoctoral researcher Sergey Levine and Ph.D. student Chelsea Finn.

The work is part of a new People and Robots Initiative at UC's Center for
Information Technology Research in the Interest of Society (CITRIS). The new
multi-campus, multidisciplinary research initiative seeks to keep the dizzying
advances in artificial intelligence, robotics and automation aligned to human
needs.

"Most robotic applications are in controlled environments where
objects are in predictable positions," said Darrell. "The challenge
of putting robots into real-life settings, like homes or offices, is that those
environments are constantly changing. The robot must be able to perceive and
adapt to its surroundings."

Neural inspiration

Conventional, but impractical, approaches to helping a robot make its way
through a 3D world include pre-programming it to handle the vast range of
possible scenarios or creating simulated environments within which the robot
operates.

Instead, the UC Berkeley researchers turned to a new branch of artificial
intelligence known as deep learning, which is loosely inspired by the neural
circuitry of the human brain when it perceives and interacts with the world.

"For all our versatility, humans are not born with a repertoire of
behaviors that can be deployed like a Swiss army knife, and we do not need to
be programmed," said Levine. "Instead, we learn new skills over the
course of our life from experience and from other humans. This learning process
is so deeply rooted in our nervous system, that we cannot even communicate to
another person precisely how the resulting skill should be executed. We can at
best hope to offer pointers and guidance as they learn it on their own."

In the world of artificial intelligence, deep learning programs create
"neural nets" in which layers of artificial neurons process
overlapping raw sensory data, whether it be sound waves or image pixels. This
helps the robot recognize patterns and categories among the data it is
receiving. People who use Siri on their iPhones, Google's speech-to-text
program or Google Street View might already have benefited from the significant
advances deep learning has provided in speech and vision recognition.

Applying deep reinforcement learning to motor tasks has been far more
challenging, however, since the task goes beyond the passive recognition of
images and sounds.

"Moving about in an unstructured 3D environment is a whole different
ballgame," said Finn. "There are no labeled directions, no examples
of how to solve the problem in advance. There are no examples of the correct
solution like one would have in speech and vision recognition programs."

Practice makes perfect

In the experiments, the UC Berkeley researchers worked with a Willow Garage
Personal Robot 2 (PR2), which they nicknamed BRETT, or Berkeley Robot for the
Elimination of Tedious Tasks.

They presented BRETT with a series of motor tasks, such as placing blocks
into matching openings or stacking Lego blocks. The algorithm controlling
BRETT's learning included a reward function that provided a score based upon
how well the robot was doing with the task.

BRETT takes in the scene, including the position of its own arms and hands,
as viewed by the camera. The algorithm provides real-time feedback via the
score based upon the robot's movements. Movements that bring the robot closer
to completing the task will score higher than those that do not. The score feeds
back through the neural net, so the robot can learn which movements are better
for the task at hand.

This end-to-end training process underlies the robot's ability to learn on
its own. As the PR2 moves its joints and manipulates objects, the algorithm calculates
good values for the 92,000 parameters of the neural net it needs to learn.

With this approach, when given the relevant coordinates for the beginning
and end of the task, the PR2 could master a typical assignment in about 10
minutes. When the robot is not given the location for the objects in the scene
and needs to learn vision and control together, the learning process takes
about three hours.

Abbeel says the field will likely see significant improvements as the
ability to process vast amounts of data improves.

"With more data, you can start learning more complex things," he
said. "We still have a long way to go before our robots can learn to clean
a house or sort laundry, but our initial results indicate that these kinds of
deep learning techniques can have a transformative effect in terms of enabling
robots to learn complex tasks entirely from scratch. In the next five to 10
years, we may see significant advances in robot learning capabilities through
this line of work."







Story Source:

The above story is based on materials provided
by University of California - Berkeley. The original article
was written by Sarah Yang. Note: Materials may be edited for content
and length.

http://www.sciencedaily.com/releases/2015/05/150522105402.htm

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