Robots: Crash Course Computer Science #37

Robots: Crash Course Computer Science #37


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.

100 Replies to “Robots: Crash Course Computer Science #37”

  1. I'm surprised that you did not mention Sophia (Hanson Robotics) that was recently granted citizenship in Saudi Arabia.

  2. Boston Dynamics have some more impressive humanoid robots. Also isn't the point of Asimov's laws that it's hard to get good rules for AI safety?

  3. Wait a minute… 'Robota' is not a 'Slavic language word for a forced laborer'. In many Slavic languages, 'robota' simply means work, job or alternatively hard labor but definitely not a forced laborer. Maybe in Czech it does also mean something else. Not sure.

  4. I wrote a paper about self-driving cars about 6 months before Google (Alphabet now) announced a self-driving car. If you have the idea, the technology can make it happen, just may take centuries.

  5. When talking about robot ethics one should always mention "With Folded Hands." Possibly the most frightening of all the robot stories.

  6. So.. a few inaccuracies in this video. First, Isaac Asimov's point was that those laws DIDN'T WORK, so bringing them up as a good "general framework" is deeply ironic. Second, why show decade old footage of DARPA challenges when modern robots accomplish those tasks pretty well? Why no footage of Boston Dynamics? Finally, what you described in relation to Androids is called the "uncanny valley", and is widely regarded as the last step before something becomes believable – we are much closer than you suggest in this video to Androids appearing and behaving humanly. This channel is normally pretty good, but this episode was a bit lacking and misleading I think.

  7. "robots fail at things like petting a dog … these are all active areas of research" good good … soon my doge love bots will be prefect

  8. Why is it so shocking that robots have trouble doing things humans take for granted? Humans have millions of years of natural selection debugging our software; robots have only been around for a few decades.

  9. Currently wrapping up a semester of controller design! You guys did a really good job explaining the basics, middle school me would have loved this.

  10. could you please do a video on how religion (islam and christianity) spread in africa since they were animists; i wont stop commenting this under your new videos till i see a presentation 🙂

  11. Lol, "…feudal. nineteenth century Europe." – that's incredible how guys which are so smart in CS might be so ignorant in history

  12. Hey CC! idk if you guys will see this but I’ve watched a lot of you guy’s videos! I think it’s so great that there’s a actual decent YouTube channel for educational purposes and I’ve learned quite a bit! BUT I think what would make you guys channel 100x better would be to have quiz’s/tests for your videos so people can see how much smarter they are becoming and can tell if they are improving! Would love to get a response from you guys about this!

  13. a puddle on tatooine? i think not! and artoo better get that coffee to old ben (even if he has to get in some rocket booster action to do so), he deserves it!

  14. Kids want to 'play' games that travel (i.e. pokemon) why not make lots of inexpensive drones in many different types (subs, tanks, aircraft)?
    The museums & other educational institutions of the world could create clues to build interest in school subjects. Ever imagine flying over the Eiffel tower, crossing the arctic, or exploring under water caves…? Instead of waiting to connect to an online game, or selecting tools, kids would be waiting to find an empty drone in that part of the world & connect to it. They can request the builders to fit different scanning & measurement devices, or they could build & ship theirs to a charging facility near by. Build tons of jobs all across the globe.

  15. I heard that the word robot meant "helper", and the guy who coined that term actually got the idea from the Jewish mythology of Golems.

  16. "Azimov"? Asimov. And he didn't invent the three laws as some kind of solution for the problem of robotic ethics – they were a really rough starting point, and the stories he wrote based on them were almost all about how those laws went wrong and were not adequate on their own.

  17. Very strange episode. A bit of history with some hiccups (e.g. The Czech word 'Robot'); You mention Autonomous Cars, but no UAVs or AUVs; the terminator is mentioned and how we are trying to make androids, but nothing about how robots are used in healthcare, warehouses or factories (their main use up to date). To top it all of sudden basics of Control theory and PID controllers. Very strange episode.

  18. I feel like the categorization of A[z]imov's laws of robotics is a bit glib. If you read his robot-centric stories, they are usually all about mishaps and problems with the interpretation of the laws, which are seen by more naive characters as perfectly valid and complete rulesets.

    If anything, the laws read more to me like the parody product of a strawman overambitious engineer or philosopher that takes themselves too seriously.

  19. Once wars are being fought by robots it will still be down to, who can kill the most opposing humans*, and/or who runs the opposition out of resources first. SSDD.

    *Most being, however many whichever side is willing to give up, before saying "uncle".

  20. Barf! No, not me, I mean the mog. You know, the half-man, half-dog, from Spaceballs! He's his own best friend. Played by the late John Candy.

  21. The first mentioning of an automaton or robot thet I know of is in the Iliad. Not quite the place one would expect to find a robot. Hephaistos had some automatons working as servants.

  22. You will never take man off the battlefield. Who do you think robots will target. When we get their their will be robots on both sides. May the best A.I win

  23. When you started talking about biped robots failing to walk, I was expecting some minutes dedicated to Force Control. Nonetheless, it was an amazing episode!

  24. Are we there yet?
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  25. Girl, your voice is good but your body is not appealing good for brain (from psychology perspective, work on yourself <gym>..it will be beneficial for your channel)

  26. Dear Carrie, thank you for your useful and great videos.
    I have a question please,
    How can I make wonderful videos like yours? I mean design and the cartoon stuff.
    Thank you.

  27. "Lethal Autonomous Weapons" is a fair description, but also a terrifying acronym; as in "Obey the L.A.W. or get shot".

  28. never make super computer ai, or its going to take over the world same way as skynet.
    better make a general android ai that the worse thing they do is from detroit become human.

  29. We should use machine learning in order to teach robots about morality and ethics, whilst using variations of the Three Laws as a set of training wheels until they are fully matured. We should also be teaching ethics and morality to bots/agents on the internet, ESPECIALLY copyright/demonitization bots.

  30. Robotic technology is machine intelligence which is different from human intelligence.

    robotic should have their unique design not using current CPU, all heading toward wrong direction.

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