A robot teaching itself to walk like a human toddler
A robot teaching itself to walk like a human toddler
Kate Drew, special to CNBC.com
9 Hours Ago
Will robots soon be able to teach themselves ...
everything?
There's a robot in California teaching itself to walk.
Its name is Darwin, and like a toddler, it teeters back and forth in a UC
Berkeley lab, trying and falling, and then trying again before getting it
right. But it's not actually Darwin doing all this. It's a neural network
designed to mimic the human brain.
Darwin's baby steps speak to what many researchers
believe will be the greatest leap in robotics — a kind of general machine
learning that allows robots to adapt to new situations rather than respond to
narrow programming.
Developed by Pieter Abbeel and his team at UC Berkeley's
Robot Learning Lab, the neural network that allows Darwin to learn is not
programmed to perform any specific functions, like walking or climbing stairs.
The team is using what's called "reinforcement learning" to try and
make the robots adapt to situations as a human child would.
Like a child's brain, reinforcement technology invokes
the trial-and-error process.
"Imagine learning a new skill, like how to ride a
bike," said John Schulman, a Ph.D. candidate in computer science at UC
Berkeley in Abbeel's group. You're going to fall a lot, but then, "after
some practice, you figure it out."
Robots are pretty good at walking on flat ground, but
anytime a variable is introduced, like a step or a slope, they often can't
adapt.
Earlier this year, at the DARPA Robotics Challenge, some
of the most high-tech robots in the world competed through a set of obstacles
designed to mimic real-world disaster situations, like Fukushima. Nearly all of
them failed, prompting a parade of GIFs on the Internet depicting falling
robots.
For typically structured settings, like in factories,
robots are programmed to repeat the same function over and over again, said
Sergey Levine, another scientist working with Abbeel. For complex environments
that might change, they need to be more sophisticated and able to adapt, Levine
said.
To enable the robots to adapt, the team at UC Berkeley is
developing technology that doesn't address specific behaviors.
"We've started looking at much less restrictive
representations," Levine said. "We are basically not telling the
robot anything about doing the task." Instead, they are using large neural
networks that are general purpose. "It's kind of like the difference
between a circuit built for one specific job," he explained, "and a
general-purpose computer."
This approach enables the team to explore other
functionalities, as well.
"There's very little in these algorithms that's
specific to [locomotion]," Levine said. "In reality, these methods
are really designed from the ground up to be general." They aren't aimed
at walking, or grasping, or doing the dishes — but can be applied to all of
those things.
Less restrictive technology is also apt to make robots
cheaper to build.
"Right now if, for example, you have a company that
builds robots, for every piece of hardware that you build, you also have to
figure out how you are going to manually control it," Levine said. If a
robot can learn on its own, the manual inputs needed for it to function would
be fewer, thereby making it less costly to make.
In real scenarios, it's really difficult to anticipate
every situation in advance, and it's nearly impossible to program for every
situation, said Martial Hebert, a professor at The Robotics Institute at
Carnegie Mellon University. "The grand challenge is to be able to teach
robots how to do end-to-end tasks."
In an ideal world, a robot will be able to learn simply
by demonstration, with no need for expensive, time-consuming programming,
Herbert said, adding, "It will be much easier to configure them," he
said.
That, in turn, could help lower the purchase price for
robots, making them accessible to everyday consumers — which right now they
aren't. Boston Dynamics' Atlas robot, used by several DARPA teams in the
Challenge, carries a price tag of over $1 million.
Bringing Star Wars' C-3PO to Earth
"To get robots into our everyday environments will
require equipping them with the ability to deal with a very large range of
variation," Abbeel said. "My belief is that the most practical way to
equip robots with such skills is to equip them with the ability to learn."
The scientists at UC Berkeley hope to move closer to a
world where robots are autonomous, nimbly performing many functions typically
done by humans. In the future, robots may be able to provide care for the
elderly, conduct rescue efforts, clean up in disaster areas and even deliver
mail, Schulman said.
There are still many situations that will need remote
human control, like for operations that need to be executed very precisely,
Hebert said. But the recent research suggests a new direction for the robotics
field. "It's moving away from pre-programming of robots and toward robots
that are more and more able to generalize from example," he said.
Abbeel's team is attempting to flesh out this shift.
"More work is necessary to move these results from simulation to the real
world, but I think eventually this research will have a very big impact on
robotics," Schulman said. "It might be the path to actual humanoid
robots, like Star Wars' C-3PO."
— Kate Drew, special to CNBC.com
Comments
Post a Comment