Hi everyone!
Welcome to the new Seeker Elements set.
We’re still going to be covering all of the mind bogglingly awesome discoveries in
the science and tech world, we’ve just got a fun new way to do it.
And what better way to kick things off then with an update from the field of machine learning
and robotics.
If you’ve ever seen a baby kick and squirm involuntarily or watched a small child clumsily
try to do a complex physical task, you know that it takes humans years to develop fine
motor skills.
So it makes sense that intuitive, fluid motions are hard to get robots to do on their own...up
until now.
And get this--the robots are teaching themselves.
A new robotic demonstration from a company called Open AI uses something called machine
learning, specifically a neural network, to allow this robotic hand to perform a complicated
series of independent object manipulations.
That means the motions you’re seeing here?
The robot is doing that by itself, without any input from or control by a human, and
without any DIRECT programming to perform each action.
But first--what is machine learning?
Machine learning is a subset of artificial intelligence.
It’s getting computers to perform tasks without being explicitly programmed to do
them.
Take one of the most advanced robots we have today, a robot that helps us perform surgeries.
This is a traditionally programmed robot, that has to be explicitly told what to do
every time.
The programmer has to write: “if this happens, the machine will do that”, for every step
of that robot’s action.
For tasks where that would be prohibitively time-intensive, machine learning algorithms
can be used instead.
These are algorithms that you can expose to vast quantities of data, from which they can
‘learn’ certain criteria and identify patterns.
So how is this applied in something like the robotic hand from Open AI?
In this situation the main data sets are all the different positions of the hand and the
block.
But the combination of all of these possibilities gives us way too many options for the robot
to practice in real life so that it can ‘learn’ each one.
So instead, the researchers used a massive amount of computing power to simulate training
the hand --they designed a virtual space in which the robot could experience myriad hand
and block positions at an accelerated pace inside a computer model.
The team estimates they exposed the robotic hand to about 100 years of trial and error
experience in just 50 hours of simulation . In addition to letting the hand ‘practice’,
the researchers also randomized some aspects of the simulation, variables like the size
and slipperiness of the block and even the force of gravity.
While the simulation couldn’t reproduce everything the robot would encounter when
handling the cube in the ‘real world’, these variables made it more likely that the
simulation practice would be useful in real-world conditions.
Because of all the variables involved, the research team used a kind of machine learning
algorithm with a ‘memory’ --based loosely on the way a human memory would work--making
this particular algorithm a neural network: a kind of machine learning loosely based on
the human brain and its logic structures.
They then transferred all of this ‘learned’ information to the real-life robotic hand,
which is equipped with a set of cameras that can estimate the object’s position and orientation.
The end result?
Simply ask the hand to manipulate the object in a certain way--say, to reorient the block
with its purple side up--and it’ll do it.
You ask for an outcome, and the robot can provide that with no further input from you
because it taught itself the series of motions it needed to get there.
As you can see*, this robot developed motions you may recognize from your own hands, just
by learning what motions were most efficient and effective at moving the block without
dropping it just with input from visual stimuli and joint sensors on each hand.
Other experts in the field of robotics and machine learning state that while this example
is exciting and comparatively elegant, it’s not necessarily new.
It’s also still quite limited to an object of convenient size in a hand that’s facing
up, so it’s not dealing with as many challenges as a machine-learning robot being asked to
complete a task that would be useful in say, an assembly line.
So while robotics is still catching up to human capabilities, it is making strides.
This work from Open AI shows us that developing machine learning algorithms and neural networks
could help us make more precise and dexterous robots, that not only help us with the things
we don’t want to do or can’t do--but teach themselves how to do it.
For more on AI, subscribe to Seeker, and check out this video here about how AI is being
used in the real world to monitor your data.
And fun fact, some machine learning algorithms are also teaching themselves as they go.
As they complete the task they taught themselves to do, they’re learning from their mistakes
and evolving their own algorithm to make it more accurate--with some small guidance from
human programmers.
Thanks for watching Seeker.