Robots: Crash Course Computer Science #37 Video Lecture | Introduction to Computer Science: An Overview - Software Development

Hi, I’m Carrie Anne, and welcome to CrashCourse
Computer Science!
Today we’re going to talk about robots.
The first image that jumps to your mind is
probably a humanoid robot, like we usually
see in shows or movies.
Sometimes they’re our friends and colleagues,
but more often, they’re sinister, apathetic
and battle-hardened.
We also tend to think of robots as a technology
of the future.
But the reality is: they’re already here
– by the millions – and they’re our
workmates, helping us to do things harder,
better, faster, and stronger.
INTRO
There are many definitions for robots, but
in general, these are machines capable of
carrying out a series of actions automatically,
guided by computer control.
How they look isn’t part of the equation
– robots can be industrial arms that spray
paint cars, drones that fly, snake-like medical
robots that assist surgeons, as well as humanoid
robotic assistants.
Although the term “robot” is sometimes
applied to interactive virtual characters,
it’s more appropriate to call these “bots”,
or even better, “agents.”
That’s because the term “robot” carries
a physical connotation a machine that lives
in and acts on the real world.
The word “robot” was first used in a 1920
Czech play to denote artificial, humanoid
characters.
The word was derived from “robota”, the
slavic-language word for a forced laborer,
indicating peasants in compulsory service
in feudal, nineteenth century Europe.
The play didn’t go too much into technological
details.
But, even a century later, it’s still a
common portrayal: mass-produced, efficient,
tireless creatures that look human-esque,
but are emotionless, indifferent to self-preservation
and lack creativity.
The more general idea of self-operating machines
goes back even further than the 1920s.
Many ancient inventors created mechanical
devices that performed functions automatically,
like keeping the time and striking bells on
the hour.
There are plenty of examples of automated
animal and humanoid figures, that would perform
dances, sing songs, strike drums and do other
physical actions.
These non-electrical and certainly non-electronic
machines were called automatons.
For instance, an early automaton created in
1739 by the Frenchman Jacques de Vaucanson
was the Canard Digerateur or Digesting Duck,
a machine in the shape of a duck that appeared
to eat grain and then defecate.
In 1739 Voltaire wrote,
“Without the voice of le Maure and Vaucanson’s
duck, you would have nothing to remind you
of the glory of France.”
One of the most infamous examples was the
“Mechanical Turk”: a chess-playing, humanoid
automaton.
After construction in 1770, it toured all
over Europe, wowing audiences with its surprisingly
good chess-playing.
It appeared to be a mechanical, artificial
intelligence.
Unfortunately, it was a hoax – there was
a dainty human stuffed inside the machine.
The first machines controlled by computers
emerged in the late 1940s.
These Computer Numerical Control, or CNC,
machines, could run programs that instructed
a machine to perform a series of operations.
This level of control also enabled the creation
of new manufactured goods, like milling a
complex propellor design out of a block of
aluminum – something that was difficult
to do using standard machine tools, and with
tolerances too small to be done by hand.
CNC machines were a huge boon to industry,
not just due to increased capability and precision,
but also in terms of reducing labor costs
by automating human jobs – a topic we’ll
revisit in a later episode.
The first commercial deployment was a programmable
industrial robot called the Unimate, sold
to General Motors in 1960 to lift hot pieces
of metal from a die casting machine and stack
them.
This was the start of the robotics industry.
Soon, robots were stacking pallets, welding
parts, painting cars and much more.
For simple motions – like a robotic gripper
that moves back and forth on a track – a
robot can be instructed to move to a particular
position, and it’ll keep moving in that
direction until the desired position is reached,
at which point it’ll stop.
This behavior can be achieved through a simple
control loop.
First, sense the robot position.
Are we there yet?
Nope.
So keep moving.
Now sense position again.
Are we there yet?
Nope, so keep moving.
Are we there yet?
Yes!
So we can stop moving, and also please be
quiet!
Because we’re trying to minimize the distance
between the sensed position and the desired
position, this control loop is, more specifically,
a negative feedback loop.
A negative feedback control loop has three
key pieces.
There’s a sensor, that measures things in
the real world, like water pressure, motor
position, air temperature, or whatever you’re
trying to control.
From this measurement, we calculate how far
we are from where we want to be – the error.
The error is then interpreted by a controller,
which decides how to instruct the system to
minimize that error.
Then, the system acts on the world though
pumps, motors, heating elements, and other
physical actuators.
In tightly controlled environments, simple
control loops, like this, work OK.
But in many real world applications, things
are a tad more complicated.
Imagine that our gripper is really heavy,
and even when the control loop says to stop,
momentum causes the gripper to overshoot the
desired position.
That would cause the control loop to take
over again, this time backing the gripper up.
A badly tuned control loop might overshoot
and overshoot and overshoot, and maybe even
wobble forever.
To make matters worse, in real world settings,
there are typically external and variable
forces acting on a robot, like friction, wind
and items of different weight.
To handle this gracefully, more sophisticated
control logic is needed.
A widely used control-loop, feedback mechanism is a proportional–integral–derivative cotnroller.
That’s a bit of a mouthful, so people call
them PID controllers.
These used to be mechanical devices, but now
it’s all done in software.
Let’s imagine a robot that delivers coffee.
Its goal is to travel between customers at
two meters per second, which has been determined
to be the ideal speed that’s both safe and
expedient.
Of course, the environment doesn’t always
cooperate.
Sometimes there’s wind, and sometimes there's
uphills and downhills and all sorts of things
that affect the speed of the robot.
So, it’s going to have to increase and decrease power to its motors to maintain the desired speed.
Using the robot's speed sensor, we can keep
track of its actual speed and plot that alongside
its desired speed.
PID controllers calculate three values from
this data.
First is the proportional value, which is
the difference between the desired value and
the actual value at the most recent instant
in time or the present.
This is what our simpler control loop used
before.
The bigger the gap between actual and desired,
the harder you'll push towards your target.
In other words, it’s proportional control.
Next, the integral value is computed, which
is the sum of error over a window of time,
like the last few seconds.
This look back helps compensate for steady
state errors, resulting from things like motoring
up a long hill.
If this value is large, it means proportional
control is not enough, and we have to push
harder still.
Finally, there’s the derivative value, which
is the rate of change between the desired
and actual values.
This helps account for possible future error,
and is sometimes called "anticipatory control".
For example, if you are screaming in towards
your goal too fast, you’ll need to ease
up a little to prevent overshoot.
These three values are summed together, with
different relative weights, to produce a controller
output that’s passed to the system.
PID controllers are everywhere, from the cruise
control in your car, to drones that automatically
adjust their rotor speeds to maintain level
flight, as well as more exotic robots, like
this one that balances on a ball to move around.
Advanced robots often require many control
loops running in parallel, working together,
managing everything from robot balance to
limb position.
As we’ve discussed, control loops are responsible
for getting robot attributes like location
to desired values.
So, you may be wondering where these values
come from.
This is the responsibility of higher-level
robot software, which plans and executes robot
actions, like plotting a path around sensed
obstacles, or breaking down physical tasks,
like picking up a ball, into simple, sequential
motions.
Using these techniques, robots have racked
up some impressive achievements – they’ve
been to the deepest depths of Earth’s oceans
and roved around on Mars for over a decade.
But interestingly, lots of problems that are
trivial for many humans have turned out to
be devilishly difficult for robots: like walking
on two legs, opening a door, picking up objects
without crushing them, putting on a t-shirt,
or petting a dog.
These are tasks you may be able to do without
thinking, but a supercomputer-powered robot
fails at spectacularly.
These sorts of tasks are all active areas
of robotics research.
Artificial intelligence techniques, which
we discussed a few episodes ago, are perhaps
the most promising avenue to overcome these
challenges.
For example, Google has been running an experiment
with a series of robotic arms that spend their
days moving miscellaneous objects from one
box to another, learning from trial and error.
After thousands of hours of practice, the
robots had cut their error rate in half.
Of course, unlike humans, robots can run twenty-four
hours a day and practice with many arms at
the same time.
So, it may just be a matter of time until
they become adept at grasping things.
But, for the time being, toddlers can out-grasp
them.
One of the biggest and most visible robotic
breakthrough in recent years has been self-driving,
autonomous cars.
If you think about it, cars don’t have too
many system inputs – you can speed up or
slow down, and you can steer left or right.
The tough part is sensing lanes, reading signs,
and anticipating and navigating traffic, pedestrians,
bicyclists, and a whole host of obstacles.
In addition to being studded with proximity
sensors, these robotic vehicles heavily rely
on Computer Vision algorithms, which we discussed
in Episode 35.
We’re also seeing the emergence of very
primitive androids – robots that look and
act like humans.
Arguably, we’re not close on either of those
goals, as they tend to look pretty weird and
act even weirder.
At least we’ll always have Westworld.
But anyway, these remain a tantalizing goal
for roboticists that combine many computer
science topics we’ve touched on over the
last few episodes, like artificial intelligence,
computer vision and natural language processing.
As for why humans are so fascinated by creating
artificial embodiments of ourselves...you’ll
have to go to Crash Course Philosophy for
that.
And for the foreseeable future, realistic
androids will continue to be the stuff of
science fiction.
Militaries also have a great interest in robots
– they’re not only replaceable, but can
surpass humans in attributes like strength,
endurance, attention, and accuracy.
Bomb disposal robots and reconnaissance drones
are fairly common today.
But fully autonomous, armed-to-the-teeth robots
are slowly appearing, like the Samsung SGR-A1
sentry gun deployed by South Korea.
Robots with the intelligence and capability
to take human lives are called lethal autonomous weapons.
And they’re widely considered a complex
and thorny issue.
Without doubt, these systems could save soldiers
lives by taking them off the battlefield and
out of harm’s way.
It might even discourage war all together.
Though it’s worth noting that people said
the same thing about dynamite and nuclear
weapons.
On the flip side, we might be creating ruthlessly
efficient killing machines that don’t apply
human judgment or compassion to complex situations.
And the fog of war is about as complex and
murky as they come.
These robots would be taking orders and executing
them as efficiently as they can and sometimes
human orders turn out to be really bad.
This debate is going to continue for a long
time, and pundits on both sides will grow
louder as robotic technology improves.
It’s also an old debate – the danger was
obvious to science fiction writer Isaac Asimov,
who introduced a fictional “Three Laws of
Robotics” in his 1942 short story "Runaround".
And then, later he added a zeroth rule.
In short, it’s a code of conduct or moral
compass for robots – guiding them to do
no harm, especially to humans.
It’s pretty inadequate for practical application
and it leaves plenty of room for equivocation.
But still, Asimov’s laws inspired a ton
of science fiction and academic discussion,
and today there are whole conferences on robot
ethics.
Importantly, Asimov crafted his fictional
rules as a way to push back on “Robot as
a Menace” memes common in fiction from his
childhood.
These were stories where robots went off the
rails, harming or even destroying their creators
in the process.
Asimov, on the other hand, envisioned robots
as useful, reliable, and even loveable machines.
And it’s this duality I want to leave you
thinking about today.
Like many of the technologies we’ve discussed
throughout this series, there are benevolent
and malicious uses.
Our job is to carefully reflect on computing's
potential and peril, and wield our inventive
talents to improve the state of the world.
And robots are one of the most potent reminders
of this responsibility.
I’ll see you next week.